{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy import stats\n",
    "import gc\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SUPPRESS SCIENTIFIC NOTATION\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "#FONT SIZE FOR LABELS ON DIAGRAMS\n",
    "matplotlib.rcParams.update({'font.size': 15, 'image.cmap': 'Greys'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Download proceeded data cleaned from anomalies (users who have less than or equal to 10 subscriptions on publics and blogers)\n",
    "def download(): \n",
    "    data = pd.read_csv('finished_BD2/BD2_proceeded.csv')\n",
    "    data.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "    return data\n",
    "\n",
    "def sub_hist(data):\n",
    "    bins = np.logspace(0, 3.4, 20)\n",
    "    widths = (bins[1:] - bins[:-1])\n",
    "    hist = np.histogram(data, bins=bins)\n",
    "    hist_norm = hist[0]/widths\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.rcParams.update({'font.size': 15})\n",
    "    plt.xlim(10, data.max())\n",
    "    plt.bar(bins[:-1], hist_norm, widths, color='grey')\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    plt.xlabel('Number of Subscriptions $k$', fontsize = 10)\n",
    "    plt.ylabel('Number of Users Having $k$ Subscriptions', fontsize = 10)\n",
    "    plt.show()\n",
    "    return 0\n",
    "\n",
    "def avg_prediction(clf):\n",
    "    \n",
    "    print('Fraction of instances of the class 1:', \n",
    "          round(data[data[clf] == 1].shape[0]/data.shape[0], 3))\n",
    "    \n",
    "    print('Fraction of instances of the class 2:', \n",
    "          round(data[data[clf] == 2].shape[0]/data.shape[0], 3))\n",
    "    \n",
    "    print('Fraction of instances of the class 3:', \n",
    "          round(data[data[clf] == 3].shape[0]/data.shape[0], 3))\n",
    "    \n",
    "    print('Fraction of instances of the class 4:', \n",
    "          round(data[data[clf] == 4].shape[0]/data.shape[0], 3))\n",
    "    \n",
    "    return 0\n",
    "\n",
    "def age_slice(clf, age, norm):\n",
    "    data_temp = data[data['Age'] == age]\n",
    "    N = data_temp.shape[0]\n",
    "    if N > 0:\n",
    "        if norm == 0:\n",
    "            ratio1 = data_temp[data_temp[clf] == 1].shape[0]  \n",
    "            ratio2 = data_temp[data_temp[clf] == 2].shape[0]  \n",
    "            ratio3 = data_temp[data_temp[clf] == 3].shape[0]  \n",
    "            ratio4 = data_temp[data_temp[clf] == 4].shape[0]  \n",
    "        else:\n",
    "            ratio1 = data_temp[data_temp[clf] == 1].shape[0] / N  \n",
    "            ratio2 = data_temp[data_temp[clf] == 2].shape[0] / N  \n",
    "            ratio3 = data_temp[data_temp[clf] == 3].shape[0] / N  \n",
    "            ratio4 = data_temp[data_temp[clf] == 4].shape[0] / N \n",
    "    else:\n",
    "        ratio1=0\n",
    "        ratio2=0\n",
    "        ratio3=0\n",
    "        ratio4=0\n",
    "    return N, ratio1, ratio2, ratio3, ratio4\n",
    "\n",
    "def age_slices(lower_age, upper_age, clf, norm):\n",
    "    massiv = [[], [], [], [], [], []] \n",
    "    for i in range(lower_age, upper_age):\n",
    "        N, ratio1, ratio2, ratio3, ratio4 = age_slice(clf, i, norm)\n",
    "        massiv[0].append(i)\n",
    "        massiv[1].append(N)\n",
    "        massiv[2].append(ratio1)\n",
    "        massiv[3].append(ratio2)\n",
    "        massiv[4].append(ratio3)\n",
    "        massiv[5].append(ratio4)\n",
    "    return massiv\n",
    "\n",
    "def plotting_age_structure(lower_age, upper_age, clf, norm, labelsize):\n",
    "    massiv = age_slices(lower_age, upper_age, clf, norm)\n",
    "    rnd = massiv[2:6]\n",
    "    yrs = massiv[0]\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    ax.xaxis.set_tick_params(labelsize=labelsize)\n",
    "    ax.yaxis.set_tick_params(labelsize=labelsize)\n",
    "    ax.stackplot(yrs, rnd, labels=['P.N. Grudinin', 'V.V. Zhirinovsky', 'A.A. Navalny', 'V.V. Putin'], \n",
    "                 colors=['lightsalmon','green','purple','orangered'])\n",
    "    if norm == 1:\n",
    "        ax.legend(loc='lower center')\n",
    "    else:\n",
    "        ax.legend(loc='upper right') \n",
    "    \n",
    "    ax.set_xlim(xmin=yrs[0], xmax=yrs[-1])\n",
    "    if norm == 1:\n",
    "        ax.set_ylim(ymin=0, ymax=1)\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Town</th>\n",
       "      <th>Number of Subscriptions</th>\n",
       "      <th>$c_{1}^{NN}$</th>\n",
       "      <th>$c_{2}^{NN}$</th>\n",
       "      <th>$c_{3}^{NN}$</th>\n",
       "      <th>$c_{4}^{NN}$</th>\n",
       "      <th>$c_{1}^{LG}$</th>\n",
       "      <th>$c_{2}^{LG}$</th>\n",
       "      <th>$c_{3}^{LG}$</th>\n",
       "      <th>$c_{4}^{LG}$</th>\n",
       "      <th>LG Prediction</th>\n",
       "      <th>NN Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Верхняя Пышма</td>\n",
       "      <td></td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>Великий Новгород</td>\n",
       "      <td></td>\n",
       "      <td>13.000</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.263</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>Железнодорожный (Балашиха)</td>\n",
       "      <td></td>\n",
       "      <td>15.000</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.359</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>Петропавловск-Камчатский</td>\n",
       "      <td></td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.239</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td></td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.310</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex                        City Home Town  Number of Subscriptions  \\\n",
       "0   22    1               Верхняя Пышма                             12.000   \n",
       "1   22    2            Великий Новгород                             13.000   \n",
       "2   22    2  Железнодорожный (Балашиха)                             15.000   \n",
       "3   22    2    Петропавловск-Камчатский                             12.000   \n",
       "4   22    1             Санкт-Петербург                             20.000   \n",
       "\n",
       "   $c_{1}^{NN}$  $c_{2}^{NN}$  $c_{3}^{NN}$  $c_{4}^{NN}$  $c_{1}^{LG}$  \\\n",
       "0         0.319         0.320         0.309         0.051         0.275   \n",
       "1         0.142         0.181         0.560         0.118         0.264   \n",
       "2         0.208         0.134         0.101         0.556         0.256   \n",
       "3         0.181         0.381         0.132         0.306         0.261   \n",
       "4         0.223         0.402         0.206         0.169         0.268   \n",
       "\n",
       "   $c_{2}^{LG}$  $c_{3}^{LG}$  $c_{4}^{LG}$  LG Prediction  NN Prediction  \n",
       "0         0.244         0.226         0.255              1              2  \n",
       "1         0.233         0.240         0.263              1              3  \n",
       "2         0.219         0.166         0.359              4              4  \n",
       "3         0.283         0.217         0.239              2              2  \n",
       "4         0.236         0.186         0.310              4              2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = download() \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11820412, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      5909624\n",
       "1      5907207\n",
       "0         3580\n",
       "127          1\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population pyramide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_value = data[data['Age'] > 0]['Age'].min()\n",
    "max_value = data[data['Age'] > 0]['Age'].max()\n",
    "\n",
    "hist_male = np.histogram(data[(data['Age'] > 0) & (data['Sex'] == 2)]['Age'], bins=10, \n",
    "                         range=(min_value, max_value))\n",
    "\n",
    "hist_female = np.histogram(data[(data['Age'] > 0) & (data['Sex'] == 1)]['Age'], bins=10, \n",
    "                           range=(min_value, max_value))\n",
    "\n",
    "males = hist_male[0]\n",
    "females = hist_female[0]\n",
    "ages = hist_male[1]\n",
    "\n",
    "age_intervales = []\n",
    "for i in range(len(ages) - 1):\n",
    "    if i == 0:\n",
    "        age_intervales.append(str(int(ages[i])) + '-' + str(int(ages[i+1])))\n",
    "    else:\n",
    "        age_intervales.append(str(int(ages[i])+1) + '-' + str(int(ages[i+1])))\n",
    "        \n",
    "Pyr_data = pd.DataFrame(data={'age_intervales': age_intervales, 'males' : -males, 'females' : females})\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "bar_plot = sns.barplot(x=\"males\",y=\"age_intervales\", color=\"red\", label=\"Males\",data = Pyr_data)\n",
    "bar_plot = sns.barplot(x=\"females\",y=\"age_intervales\", color=\"blue\", label=\"Females\",data = Pyr_data)\n",
    "bar_plot.set(xlabel=\"Number of Users\", ylabel=\"Age Group\");\n",
    "\n",
    "plt.xticks([-3000000, -2000000, -1000000, 0, 1000000, 2000000, 3000000], \n",
    "           [3, 2, 1, 0, 1, 2, 3])\n",
    "\n",
    "plt.xlabel('Millions of Users')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cities, home towns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Москва             1530200\n",
       "Санкт-Петербург     938225\n",
       "                    642986\n",
       "Екатеринбург        223624\n",
       "Новосибирск         212821\n",
       "                    ...   \n",
       "Немда                    1\n",
       "Новое на Комье           1\n",
       "Кочкурга                 1\n",
       "Боловино                 1\n",
       "Любец                    1\n",
       "Name: City, Length: 29771, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['City'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                              6453795\n",
       "Москва                                         193063\n",
       "Санкт-Петербург                                 74261\n",
       "Новосибирск                                     34082\n",
       "Екатеринбург                                    30970\n",
       "                                               ...   \n",
       "Камешково Владимирская обл                          1\n",
       "Сколько глобус не крути фик вам меня найти          1\n",
       "Валга (Эстония)                                     1\n",
       "Кострома  -  Дзержинск                              1\n",
       "город Свободный Амурская область                    1\n",
       "Name: Home Town, Length: 391470, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Home Town'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Subscriptions (in log-log scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_hist(data['Number of Subscriptions'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LG and NN predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of instances of the class 1: 0.256\n",
      "Fraction of instances of the class 2: 0.094\n",
      "Fraction of instances of the class 3: 0.123\n",
      "Fraction of instances of the class 4: 0.527\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_prediction('LG Prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of instances of the class 1: 0.168\n",
      "Fraction of instances of the class 2: 0.224\n",
      "Fraction of instances of the class 3: 0.228\n",
      "Fraction of instances of the class 4: 0.381\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_prediction('NN Prediction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citites arrangement according to the supporting of President V.V. Putin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities_counts = data['City'].value_counts()\n",
    "cities_counts = cities_counts.reset_index()\n",
    "cities_counts.rename(columns={'index': 'City', 'City': 'count'}, inplace=True)\n",
    "cities_counts = cities_counts[cities_counts['count'] > 3000]  #Consider only cities where more than 3000 users from the sample live\n",
    "#cities_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LG prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70774996c03b45489de28338e96214fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=362), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Score</th>\n",
       "      <th>Voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Каспийск</td>\n",
       "      <td>0.737</td>\n",
       "      <td>3830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Кизляр</td>\n",
       "      <td>0.728</td>\n",
       "      <td>3260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Махачкала</td>\n",
       "      <td>0.717</td>\n",
       "      <td>43812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Хасавюрт</td>\n",
       "      <td>0.710</td>\n",
       "      <td>5321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Владикавказ</td>\n",
       "      <td>0.700</td>\n",
       "      <td>23618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Score  Voters\n",
       "0     Каспийск  0.737    3830\n",
       "1       Кизляр  0.728    3260\n",
       "2    Махачкала  0.717   43812\n",
       "3     Хасавюрт  0.710    5321\n",
       "4  Владикавказ  0.700   23618"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principal_cities = []\n",
    "scores = []\n",
    "\n",
    "for i in tqdm(range(cities_counts.shape[0])):\n",
    "    data_temp = data[data['City'] == cities_counts['City'][i]]\n",
    "    principal_cities.append(cities_counts['City'][i])\n",
    "    scores.append(data_temp[data_temp['LG Prediction'] == 4].shape[0] / data_temp.shape[0])  \n",
    "\n",
    "city_analysis = pd.DataFrame({'City': principal_cities, 'Score': scores, \n",
    "                              'Voters': list(cities_counts[cities_counts['count'] > 3000]['count'])})\n",
    "del(principal_cities, scores)\n",
    "city_analysis = city_analysis.sort_values(by=['Score'], ascending=False)\n",
    "city_analysis.reset_index(drop=True, inplace=True)\n",
    "\n",
    "city_analysis.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Score</th>\n",
       "      <th>Voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Королёв</td>\n",
       "      <td>0.466</td>\n",
       "      <td>9912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Москва</td>\n",
       "      <td>0.454</td>\n",
       "      <td>1530200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Долгопрудный</td>\n",
       "      <td>0.441</td>\n",
       "      <td>3988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Дубна</td>\n",
       "      <td>0.435</td>\n",
       "      <td>4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>0.433</td>\n",
       "      <td>938225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Score   Voters\n",
       "357          Королёв  0.466     9912\n",
       "358           Москва  0.454  1530200\n",
       "359     Долгопрудный  0.441     3988\n",
       "360            Дубна  0.435     4574\n",
       "361  Санкт-Петербург  0.433   938225"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_analysis.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_analysis.to_csv('finished_BD2/city_ordered_LG.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NN prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b279db1b523f4bfabdc764640f444df8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=362), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Score</th>\n",
       "      <th>Voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Каспийск</td>\n",
       "      <td>0.587</td>\n",
       "      <td>3830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Кизляр</td>\n",
       "      <td>0.584</td>\n",
       "      <td>3260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Махачкала</td>\n",
       "      <td>0.567</td>\n",
       "      <td>43812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Донецк</td>\n",
       "      <td>0.563</td>\n",
       "      <td>4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Дербент</td>\n",
       "      <td>0.563</td>\n",
       "      <td>5658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City  Score  Voters\n",
       "0   Каспийск  0.587    3830\n",
       "1     Кизляр  0.584    3260\n",
       "2  Махачкала  0.567   43812\n",
       "3     Донецк  0.563    4324\n",
       "4    Дербент  0.563    5658"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principal_cities = []\n",
    "scores = []\n",
    "\n",
    "for i in tqdm(range(cities_counts.shape[0])):\n",
    "    data_temp = data[data['City'] == cities_counts['City'][i]]\n",
    "    principal_cities.append(cities_counts['City'][i])\n",
    "    scores.append(data_temp[data_temp['NN Prediction'] == 4].shape[0] / data_temp.shape[0])  \n",
    "    \n",
    "city_analysis = pd.DataFrame({'City': principal_cities, 'Score': scores, \n",
    "                              'Voters': list(cities_counts[cities_counts['count'] > 3000]['count'])})  #Consider only cities where more than 3000 users from the sample live\n",
    "del(principal_cities, scores)\n",
    "city_analysis = city_analysis.sort_values(by=['Score'], ascending=False)\n",
    "city_analysis.reset_index(drop=True, inplace=True)\n",
    "\n",
    "city_analysis.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Score</th>\n",
       "      <th>Voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Сосновый Бор</td>\n",
       "      <td>0.318</td>\n",
       "      <td>4629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Петрозаводск</td>\n",
       "      <td>0.317</td>\n",
       "      <td>38876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Кондопога</td>\n",
       "      <td>0.314</td>\n",
       "      <td>3636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Дубна</td>\n",
       "      <td>0.292</td>\n",
       "      <td>4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>0.278</td>\n",
       "      <td>938225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Score  Voters\n",
       "357     Сосновый Бор  0.318    4629\n",
       "358     Петрозаводск  0.317   38876\n",
       "359        Кондопога  0.314    3636\n",
       "360            Дубна  0.292    4574\n",
       "361  Санкт-Петербург  0.278  938225"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_analysis.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_analysis.to_csv('finished_BD2/city_ordered_NN.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Votting patterns as a function of age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for LG prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting_age_structure(18, 81, 'LG Prediction', 1, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7di2nnXYaAIMGDXK5nv379zNo0CA6dOjAsmXLaN68OeD499CuXTv++c9/Vt9n1apV7Nu3z+VfGMLFn6g9HGjGD60NDYq1do61tq+1tm+7du0ivZyGyzn0qtIrIiKVEhISGDVqFAsWLKCqtpaenk7Xrl3p37+/22sTExMZOXJkjUpmeno6p556Kmeffbbfa7r//vv5z3/+w+uvv06rVq1cjunfv3914AXo3bs35eXlNVocXBk6dKjXD9cNHTq0+tedOnUiJSWFPXv2APD9999z+PBhxowZU+OacePGcezYMTIzM6s/b9q0ia1btwKwb98+Pv74Y8aNGwdAhw4d6NixI9dffz0LFy7k8OHDLteyd+9ezj//fLp06cLy5curAy9AXFwcEydO5NVXX60+9sorrzBgwIDqIB0J/oTe8cC31tqNTseygOa1twvDUWktcKqmZgEtXcyZUnkuUnNJuOUr9IqIiGsTJkxg165dZGRkUFRUxFtvvcX48eMxxvMuFBMmTGD79u2sX7+egoICli1bxvjx4/1ey7p16/jrX//Kvffe63YbsdphOCEhAYCioiK383fo0MHrtbi6R9X8+/fvdzlf1edjx44BcMEFF9CxY8fq3ueFCxeSlJTEr371K8BReV69ejUpKSlMnDiRjh07csEFF/DVV1/VmPfLL79k27ZtTJw4kaZNm9ZZ63XXXcfWrVv59NNPOXbsGEuXLmXSpEle/15DwafQa4xpieMBsNpV3i1AY6B2V3ftvtst1OqlNcakAklO4yIxl4SbKr0iIlKPCy+8kA4dOpCWlsY777xDbm6u1+0JgwcPpm3btqSlpbFs2TLy8/P9bm3Iysri6quv5pxzzuHPf/6zX3N44k2Q90anTp0AavT4Ahw8eBCA1q1bA9CoUSOuvPLK6tCbnp7O8OHDawTXH//4xyxZsoTs7GxWrlzJ8ePHGT58eI15hw0bxrRp07jmmmtYtWpVnfX86Ec/4rzzzuOVV15h/vz5xMXF1alCh5uvld6RQBPqht5PgRyg+ndT2fs7HFjhNG4FcLExpoXTsXFAIfBBBOeScFNPr4iI1KNx48aMHTuWhQsXMm/ePHr16lXdo+tJVbhasGAB8+fPp0+fPpxxxhl+reP3v/89x48f5/XXX/dpe7JIOOWUU2jXrh0LFy6scXzBggW0adOGXr16VR8bP348mzdv5p133uGzzz6rtxKekJDAkCFDuPXWW9m5cyf5+fk1zj/88MP84Q9/YNSoUXz88cd1rr/uuutIT09nzpw5jBkzpkYLRCT4GnrHA5ustZnOB621RcAM4F5jzM3GmEHAwsr5ZzkNnQ0UA4uNMYMrHxp7AHiqauuxCM0l4ab2BhERcWPChAkcOHCAJUuWuKzUfvfdd8TFxTFv3jyX1+7du5elS5e6vHbr1q3ExcXV2cXA2auvvsrChQuZNGkSBw8e5LPPPqvzlZcXPf/9io+P5/7772f27Nncc889vPvuu9x1113MnTuXadOm1Qjtv/zlL0lNTeX6668nOTm5xvZi69evZ9iwYcydO5d169axaNEinnzySc455xyaNWtW575PP/00EyZM4LLLLqux4wZQXdn98ssvufbaa0P0O/ee139tMca0BQbheLuZKzNwhMl7gDbARmCItfZg1QBrbVZl8Hwex1652cDTOMJqxOaSCFB7g4hI2Pj6ZrRo0L9/f7p168aOHTtcBldrLeXl5S73fB0wYACpqans3r3bZRXT3bVVVqxw/ED4mWeeqd7yq7aMjAyPL5wIp1tuuYXS0lKef/55nnzySbp06cJzzz3H5MmTa4wzxjB27FiefPJJrrnmmur+Y4DOnTuTkpLCQw89xP79+0lJSWHw4MHMmDHD5T2NMcyZM4f8/HwuvvhiPvzww+q3ziUlJTFkyBA2bdrEwIEDQ/cb95L5Ydcxqa1v375248aNngeK7+ZOg/mPOn7dsi0sdP10qIiIeC8zM7PGj7FFIqm4uJjU1FRuu+027rvvPq+ucfc9bIz5wlrb19/1RHeDijRcam8QERFpkIqKivjyyy959dVXyc/P53e/+12klwQo9EqkOLc3lBRBeTm4ecuOiIiIxIYdO3Zwzjnn0LFjR1566SWftmULJYVeiYz8Wi/TK8qDZq62XRYREZFYcvrppxON7bORefmxSF6t0KsWBxEREQkhhV6JDIVeERERCSOFXomM2u0NCr0iIiISQgq9Ehm1K716K5uIiIiEkEKvhF95GRTVfJWhKr0iIiISSgq9En65WXWPKfSKiIhICCn0SvjV7ucFhV4REREJKe3TK+FXu58X1NMrIhJCD5oHI3Lf6XZ6QNefcsop7Nixg23bttGjR4+wXVvlggsu4IMPPuCxxx7jnnvuqXGubdu2TJ48mQceeMCvuQOxbt06LrzwQr766it+/OMfh/3+sUqVXgk/V6FXlV4REXGSkZHBjh07AJg/f37YrnXl6aefpqCgIOB5JLIUeiX81N4gIiIezJ8/n2bNmnHOOef4HFwDuba2/v37k5OTw5w5cwKaRyJPoVfCT+0NIiLiRnl5OQsWLODyyy9n0qRJZGZmsmnTppBf68pJJ53EtddeyxNPPEFxcXG94zIyMrj88svp1KkTzZo142c/+xmvv/569fnvv/8eYwzvvPNOnfV27NiRadOmAbBlyxbGjx9PamoqSUlJnHHGGTzzzDNUVFS4XacxhmeffZZ7772Xdu3a0b59e26++ebqNR87dozExEReeeWVGtdZa+nevTt//OMfffljiUkKvRJ+am8QERE31q5dy8GDBxk/fjxXXnkl8fHxXldsA7m2PnfffTcHDx5k7ty59Y7ZuXMn5557Li+//DLLli1j9OjRXHvttdX3PuWUUzj77LNZsGBBjes++OCD6vUC7N27l549e/K3v/2N5cuXc/311zN9+nQef/xxj+t88skn2bdvH6+99hpTpkzh//7v/3j22WcBaN26NSNHjqwTetetW8f333/PpEmTfPkjiUl6kE3CT+0NIiLixvz582nVqhWXXHIJCQkJDB06lLS0NP7yl79gjAnZtfXp1q0bV111FTNnzuR3v/sdcXF141NVaAVH9XTgwIHs2bOHF198kQkTJlSPefDBBykuLqZJkyYApKenc8YZZ1Q/kDZo0CAGDRpUPc+AAQMoKCjgxRdfrPMwnat1VoXaiy++mE8++YTFixdz1113AXDdddcxdOhQtm/fTvfu3QGYO3cuZ511Fn369PHrzyaWqNIr4adKr4iI1KOkpITFixczcuRIEhISAEdY3LlzJxkZGSG71pN77rmHnTt31mhZcJaVlcWtt95K165diY+PJz4+njlz5vDNN99Ujxk7diw5OTmsXLkSgLKyMhYvXsy4ceOqxxQVFTF9+nR69OhBkyZNiI+P57777uP777+nrKzM7RqHDh1a43Pv3r3Zs2dP9edBgwbRtWtXXn31VQByc3N54403uPbaa337w4hRCr0SfurpFRGReqxYsYLs7GwuvfRSsrOzyc7O5oILLqBJkyYe2xQCudaTnj17cuWVVzJjxgyX/bXXXHMN6enpTJkyhXfffZcNGzYwadIkioqKqsd07tyZAQMGkJ6eDsB7773HkSNHalSJ7777bp544gluuOEGli9fzoYNG6r7fZ3ncqVVq1Y1PickJNS4xhjDtddey6uvvoq1lgULFlBeXs6vf/1r3/9AYpBCr4Sf2htERKQeVeF0zJgxpKSkkJKSQmpqKsXFxSxcuJDy8vKQXOuN++67j61bt7Jo0aIax4uKinj77bd58MEHmTx5MhdddBF9+/Z1GY7HjRvHsmXLKCwsJD09nTPPPJPTTjut+vzChQu55ZZbuOuuuxg8eDB9+/Z12U7hr2uvvZbdu3ezdu1aXnnlFa644gpSUlKCNn80U0+vhJ/aG0RExIX8/HyWLVvGhAkTuOGGG2qc+89//sMdd9zB+++/z5AhQ4J6rbd+8pOfMHz4cB577DGstdXHi4uLqaioqO7TBUfrwNKlS+v0EY8ZM4bbbruNJUuWsGTJkjp9uoWFhTXmKS8vJy0tze8115aamsrQoUOZPn06H3/8cXWrxYlAoVfCz1WlV+0NIiInvLfeeouCggJuu+02zjnnnBrnzj33XB599FHmz5/PkCFD2LlzJ6eeeir/+Mc/uPrqqwO61hf33XdfnflbtmzJL37xCx566CGSk5Np1KgRM2bMoGXLluTk5NQY2759ey644AL+9Kc/kZ2dzdixY2ucHzJkCC+88AI9evSgdevWvPDCC263SvPHddddx5gxYzj55JMD+ktArFHolfBTpVdEJKwCfR1wuMyfP5/TTjutTqgEiI+PZ+zYscybN4+///3vWGspLy+vbiEI5FpfnH322QwZMoTVq1fXOD5v3jx+//vfc/XVV9OmTRsmT55MQUEBzz//fJ05xo8fz/XXX0+/fv3o1q1bjXOzZs3ixhtv5Oabb6Zp06ZMnDiRkSNH1qleB+JXv/oVcXFxTJw4kUaNTpxOV+Ncnpea+vbtazdu3BjpZTQ8w5tBca3XOTZqBCvKwM/tZEREBDIzM+nVq1eklyFRbvny5fzqV7/im2++oUePHpFeTg3uvoeNMV9Ya/v6O/eJE+8lOpSV1g28ABUVUBTG95oveRYO7Ajf/URERCJs3759fPDBB0ydOpVLL7006gJvqCn0Sni5am2oEs6+3lf/DNecCvcOg0/fggCf6BUREYl2c+bMYdCgQSQmJjJr1qxILyfsFHrFe7u3wuuPBDaHq4fYqoSrr7ekGApyHNXljSvhgSvg6lPgtYfg6L7wrEFERCTMHnjgAcrKyli/fj2nnHJKpJcTdgq94p2lf4Obfg6fvx3YPO4qveEKvdkH6x47vBv+OR1+0xUeHAUb3wX1u4uIiDQY2r1B3Ms6CE9OgvXLHZ+P7Q9svmhob8g+VP+58jL4ZInj66RT4bnPIblNeNYlIiIiIaNKr9QvYync0OeHwAuQdSCwOfOy6j8Xtkqvm9DrbN93sP3L0K5FREREwkKht6HLzYKvPnL0sXqrMB+evgGmj4Djh2ueKy2BnKP+rycq2hu8DL0AO/4XunWIiIhI2Ki9oaHbsxXuHAgJidCrH/Q5H356gePXCYl1x29ZD4//BvZuq3/Oo/v9/5F/NDzI5kvo3fl16NYhIiIiYaPQ29DlVrYTlBTBpnWOr9cehPgm0PNs+EllCO55Nix+Gl5/2NHX6s6x/XDKj/1bTzT09Ga5eJCtPqr0ioiINAhqb2jo6quslhbD/z6CeY/A3YNhZEvH7gWeAi8E9jBbrLU3qNIrIhI2w4cPp0+fPvWenzx5Mq1ataK4uG7L3rBhwzjzzDPrvfbGG2+kTZs2lJaWer2efv36YYzBGEN8fDynnXYaU6ZMIS/Pt/9effrppzzySN0tP6dOncrJJ5/s01ziP1V6G7pcNw+OOfNle65AQm+stTfkZcORvdC2c+jWIyISakMj9Ir3d33b+nHChAlcddVVbN68md69e9c4V15ezqJFixg1ahRNmjRxee3EiRPZunUrPXv2rHGurKyMN954g9GjRxMfH+/Tmi6++OLq/W0zMjKYPn06+/fv57XXXvN6jqrQO23atBrHb775ZsaNG+fTesR/qvQ2dO52S/DXiVTpBbU4iIiEyYgRI0hKSmL+/Pl1zq1du5aDBw8yYcIEl9deccUVJCYmkpaWVufcmjVrOHLkSL3XutO2bVv69evHgAEDmDJlCnfccQevv/46OTk5Ps9VW2pqqtvqtASXV6HXGBNnjJlqjNlmjCk2xuwxxjxda4wxxtxrjNltjCk0xnxojPmZi7l6G2PeM8YUGGP2GWMeMsY0jvRcDZa7kOmvUFV6o7GnFxR6RUTCpFmzZgwfPpz09PQ659LS0mjfvj0XXXSRy2uTk5O57LLL6r22U6dOnH/++QGv8ayzzgJg586dFBUVYYzhpZdeqjHGuW1h9uzZTJkyheLi4upWiUsuuaTOOICVK1dijOGTTz5h5MiRNGvWjFNPPbXO/OIfbyu9rwC3Ak8AQ4GpQGGtMVOB+4HHgeFAHrDGGNOxaoAxJgVYA1hgBPAQcCfwYCTnatBCUek9GsOVXmsh55KOXvEAACAASURBVIhv16ivV0QkbCZMmMC2bdv44osvqo+VlpayePFixo4dS+PGjd1em5mZyaZNm6qPFRcX8+abbzJu3DgaNQr8B9w7duwAoGNH72LEqFGjuOWWW0hISCAjI4OMjAyeeeYZt9dMmjSJc845hzfffJP+/ftz/fXX1/g9iX889vQaYy4BxgE/tdZurmdMIo5w+Rdr7fOVxzKAHcBkoKqJ5UagKTDKWpsDrDbGJAMPGGNmWmtzIjRXwxWSSu8+/6+NdOjNzYIy7x9iAFTpFREJo2HDhtGqVSvS0tKqq6qrVq0iKyvLY3vCZZddRosWLUhLS+OnP/0pACtWrOD48eN+tTYAWGspKyur7umdOXMm5557Lu3ataOoqMjj9e3bt6dLly4YY+jXr59X95w4cSJTp04F4LzzzuPtt99myZIl1b8n8Y83f+WZBLxfX+Ct9EsgGVhQdcBamw8sA4Y5jRsGrKoMqVXScITXqp85RGKuhivaKr2Rbm/wtZ8XYOdm3x70ExERvyUkJDBq1CgWLFiArfz/3vT0dLp27Ur//v3dXpuYmMjIkSNZsKD6P/ukp6dz6qmncvbZZ/u1nnnz5hEfH0/Tpk256KKL6NmzJ//85z/9mstbQ4cOrf51YmIi3bt3Z8+ePSG954nAm9B7DvCNMeZ5Y0xOZf/sYmPMSU5jTgfKgdpvNMisPOc8bovzAGvtLqDAaVwk5mq4QhF6iwsg348G/tISKK7dFeMkHJVef0JvUT4c+D74axEREZcmTJjArl27yMjIoKioiLfeeovx48djjOddKCZMmMD27dtZv349BQUFLFu2jPHjx/u9lmHDhrFhwwb++9//kpWVxbp16+jevbvf83mjVatWNT4nJCR4VVUW97zZsqwjcA2wCRgPtABmAkuMMf2s469hKUCetba81rVZQJIxJsFaW1I5zlWpL6vyHBGaq5ox5gbgBoAuXbq4mD7GhKK9ARwPszVLDu5awhJ6fXyIrcqOr6FTaP9PTkREHC688EI6dOhAWloa+/fvJzc31+v2hMGDB9O2bVvS0tI455xzyM/P97u1AaB169b07dvX5bn4+HgaNWpESUmNKEFWVggKThIwb0KvqfwaYa09CmCM2Q98AFwEvBe65YWftXYOMAegb9++wf2ZdnEhNGka1Ck9CkWlFxyhN7Wn53HO3LU2QPRWesHR19t/eHDXIiIiLjVu3JixY8eycOFC9u7dS69evbzuZ42Li2PMmDEsWLCA7du306dPH84444yQrbNTp05kZmZWHysrK+P999+vMS4hIYHS0lIqKiqC8jCd+MebP/ks4KuqwFvpY6AE6O00pnnt7cJwVFoLnKqpWUBLF/dIqTwXqblcsxVuT/tk52YY1QqmXAQLn4BdmZ6vCZS1UBD4PoIu+bNtmadKb7T29IIeZhMRCbMJEyZw4MABlixZ4rJS+9133xEXF8e8efNcXrt3716WLl3q8tqtW7cSFxdXo/fXXyNHjmTu3LnMmTOHFStWMHr06DpvjDv99NOpqKjg2WefZcOGDWzbVrvrUsLBm0pvJpDo4rgBqlLhFqAx0APY6jSmdt/tFmr10hpjUoEkp3GRmMu14z5ubeXOoicdPa2b1jq+XpwCHU+BXwyDcy6Dn14Y/Cpw/nGoCGJwd+bPw2weQ2++f2vxhb+hV9uWiUgs8/HNaNGgf//+dOvWjR07drgMrtZaysvLqXDx37kBAwaQmprK7t27XfbzurvWV4888gjHjh3j7rvvJjExkdtvv51evXrVeGPb4MGDue2223j88ce58847GTp0KCtXrgz43uIbYz08lW6M+ROOvW+7WmuPVB67AFgLnGet/bhya7CDwF+ttY9UjknCsTXYHGvttMpj9wBTKufKdZr/IaCj0zZjYZ2rPn07p9iNa1ZAL++2GKlX1kH4TVcorfuu8GpNmjqC79mXwq/+AMH48cf+72FiiPpQr/wT3PBX3675YAE86uF1i28XQULd10sGzUOj4ePFvl8X3wSW5oOb/SFFRCItMzOTXr16RXoZIn5z9z1sjPnCWuu6wdoL3iSrOcBRYJkxZrgx5tfAv4A11tqPAay1RcAM4F5jzM3GmEHAwsr5ZznNNRsoBhYbYwZXPjT2APBU1dZjEZqrfumPezXMrTdnuQ+84Oj3Xb8cnp8cvAqzpx7aQISivQFC3+Lg69vYqpQWw179OEpERCRWeWxvqKyYXgQ8h2Mf3BLgLeCPtYbOwBEm7wHaABuBIdbag05zZVUGz+dx7JWbDTyNI6xGbC63Mt6CXVugi587nBUVwNt/9+2a3KOQ0t6/+9WYJ4RPj/oTer0J4YV5kNzG97m95W97AzhaHPz9PhAREZGI8qanF2vtt8ClHsZY4NHKL3fjNuPY9SGq5nIzASycCXf+w6/LeXcu5B7z7Rpfx9cnFiu9od7BIZDQu+N/cN7o4K1FREREwkb7Znjj/dfhsB9vQqmogMVP+35dzlHPY7wRbZXeSIfe0hLHw33+0g4OIiIiMUuh1xulJf6F10/fhH3f+X5dsCq9odqjFxwB1t3b1Vxe48V6QtnTG0iVF7SDg4jEBE8PqItEq1B/7yr0emv5HN8rp4ue8O9eOcEKvSFsbwDfq72RrvT6+xBblb3bHH8BEhGJUvHx8RQW+liQEIkShYWFxMfHh2x+hV5vFebB0he8H//1p7A5w7975QapvSGUlV7wPfR6+yBbqARa6S0vg92et3cWEYmU9u3bs3fvXgoKClTxlZhhraWgoIC9e/fSvn0QHuSvh1cPskmlN5+DK+/07iUSbzzp/32C1t4Q4kqvry+oiHSlN9DQC44Wh+4/CXweEZEQSE5OBmDfvn2UlpZGeDUi3ouPj6dDhw7V38OhoNDri+OHYdU/4PKb3Y/b952jn9dfwXqQLRYrvdHc0wt6mE1Eol5ycnJIg4NIrFJ7g68WPuH4Mbc7i58O7PW/sfAgG8ReT69Cr4iIyAlLoddXB3c4Xqdbn5xjsGpuYPcIWqU3ih5kKymGkiLP40IaegN8kA20g4OIiEiMUuj1xwI3ryZ+++9QXBDY/A2x0uvtizKivb3hwPeOt+yJiIhITFHo9cf2L2H9irrHS4ph6fOBzx8rD7L5Enq9XUu0tzdUVMCuzYHPIyIiImGl0Ouv9Bl1j733Ghw7EPjchXmB7wdbXAilxYGvxR1fdm9oKKEXYIdaHERERGKNQq+/vvoQMj/74bO1sPip4M0faLU31K0NADlHoMzLLXG8bW8IZeg9fjg48+hhNhERkZij0BuINKdq74YVsDOIP/YO9GG2ULc2gCPoe/uWM2/XE6qe3tys4L1NTaFXREQk5ij0BuKzpbAr0/HrRQG8jMKVWKj0Ahzd5924SLc3BKu1ARR6RUREYpBCbyCshQUz4dv/wH/fD+7csVDpBe8fZot0e0MwQ++RPZB/PHjziYiISMjpjWyBev91OLwn+PMGWunNDVOl19vQG+n2hmCGXnA8zHbGL4M7p4iIiISMKr2BKiuF/6wJ/rwBV3qjLPRGutLrbe+xt9TiICIiElMUeqNVwD29YWpv8HbbMm/XU1IE5eX+r6c+wa706s1sIiIiMUWhN1rFyoNswW5vgNC0OAS9vUGVXhERkVii0ButTtT2BghNi4NCr4iIyAlNoTdaxUp7QygqvbEQerMPwfEjwZ1TREREQkahN1rlxkilN+sgVFR4HhfxSm+QH2QDVXtFRERiiEJvtMqJkUpvRbl3r/f1ZQu1WOjpBYVeERGRGKLQG61ipdILnlscSoqgtNj7+QpyA1tPbWWloflLgHZwEBERiRkKvdGquNARFv0VztDradsyXwNnsNsbQlHlBVV6RUREYohCbzTz92G28vLQveTBlaP73J/3NfQGu70hVKFXlV4REZGYodAbzfzdtiw/G6wN7lrc8dTe4MtDbBD8wB7st7FVyc3yHPhFREQkKij0RjN/K73heoitiqfQ21DbGwC+V4uDiIhILFDojWb+VnrD2c8LwQ+9sdLeAOrrFRERiREKvdHM30qvL9uDBUO0tzeEMvSqr1dERCQmKPRGs0B6esPpRG5vUKVXREQkJij0RrOYqfQecH8+0pXeUD3IBrBrc3gfGhQRERG/KPRGM39Db7grvaXF7t8g15B7egvz4MCO0M0vIiIiQaHQG838bW8Id6UX3Lc4NOT2BlBfr4iISAxQ6I1mfm9ZFmWhN9LtDccPB3e+2tTXKyIiEvW8Cr3GmGuMMdbF141OY4wx5l5jzG5jTKEx5kNjzM9czNXbGPOeMabAGLPPGPOQMaZxrTFhnysq+b1lWZjbGyC4ld5gtjfkH3e0X4SSQq+IiEjU87XSexHQ3+lrsdO5qcD9wOPAcCAPWGOM6Vg1wBiTAqwBLDACeAi4E3iw1n3COlfUiqVKr7s3k0WyvSGUD7FVUXuDiIhI1IvzcfwGa22dRGKMScQRLv9irX2+8lgGsAOYDEyrHHoj0BQYZa3NAVYbY5KBB4wxM621ORGaKzrFyhvZIHrbG0Ldzwuw55vQ30NEREQCEqye3l8CycCCqgPW2nxgGTDMadwwYFVlSK2ShiO8nh/BuaJTSREUFfh+XbT19PoawosLgrcNWDhCb3EBlJaE/j4iIiLiN19D73fGmDJjzFZjzO+djp8OlAPbao3PrDznPG6L8wBr7S6gwGlcJOaKXv5UeyPS3lBP6C0p8r2n1looyg98TRCe0AuO3mERERGJWt6G3v04+mJ/i6Mv9jNgtjHmj5XnU4A8a215reuygCRjTILTOFdlv6zKc5Gaq5ox5gZjzEZjzMbD+SF+AMob/jzMFk3tDf6uJVgtDuHo6QWFXhERkSjnVU+vtXYVsMrp0IrKftlpxphnQ7KyCLHWzgHmAPTtnGKhMLIL8rXSW5gH5WWhWYs70Rp6VekVERERAuvpXQS0BrrhqJw2r71dGI5Ka4G1tqrhMQto6WKulMpzVWPCPVf08rXSG4kqLzjaEQpy6x73t9UiWNuWhSv0FuR4HiMiIiIRE0jotU7/3AI0BnrUGlO773YLtXppjTGpQJLTuEjMFb18rfRGop+3iqtqryq9IiIiEgUCCb1XAkeAncCnQA4wpuqkMSYJR//vCqdrVgAXG2NaOB0bh6OH4IPKz5GYK3r5WumNxCuIq7gKvb5uV1ZFoVdERESCyKueXmPMG8B64EscldNxlV+3WmsrgCJjzAzgfmNMFo4q6h04QvUsp6lmA7cCi40xjwPdgQeAp6q2HrPWRmKu6OVrpdffkBkMrnZwiHilVw+yiYiIiPcvp9gKTAJSAQNsBq621v7LacwMHGHyHqANsBEYYq2tTh3W2ixjzCDgeRx75WYDT+MIq0Rqrqjma+iNtkqvv6E3GD29ZaXh63FWT6+IiEhU83b3hnuBez2MscCjlV/uxm3G8TrjqJoravna3hDJSm+0tTccPxy8l1x4okqviIhIVAvWG9kkVGKp0nt0X91jkWxvCFc/Lyj0ioiIRDmF3miX6+uWZVHW3hDJSm9WGENvgUKviIhINFPojXY+b1kWZe0NkezpDddDbAD56ukVERGJZgq90U779PovnO0NqvSKiIhENYXeaFda4lsAjGSlNy8bSopqHotke4N6ekVERKSSQm8s8KXaG8lKL9Tdqzei7Q0KvSIiIuKg0BsLfNm2LNKht3aLg79hMNYqvdqnV0REJKop9MYCnyq9EWxvgJqht7gQSov9mycouzeE8UG2wjyoqAjf/URERMQnCr2xwNtKb1kpFOWHdi2eOIfeQAJ4rFV6rVW1V0REJIop9MYCbyu9ka7yQs3QG8jb4YLR03v8cOBz+EKhV0REJGop9MYCbyu9ke7nheip9Obn1N1JItT0MJuIiEjUUuiNBd5WeiP5CuIqR4MUegNt0whna0MVhV4REZGopdAbC7wNvYG0EwTLsX0//DqQ9ZSXBVapDefb2Koo9IqIiEQthd5Y4G17Q0Oq9EJgLQ5ZEaj0qqdXREQkain0xoJYqvTmHHHsIgGRDb1qbxAREREnCr2xIJYqvdb+sD9uoA/WKfSKiIhIkCj0xgKvtyyLgtALP+zgEGilN5Bty44r9IqIiMgPFHpjQSzt0ws/hN5A2y0C6umNwINs6ukVERGJWgq9saC8zLHvrCfRUuk9GqRKr9obREREJEgUemOFN329qvT+QKFXREREnCj0xgpvWhyipdIbDT29Cr0iIiLiRKE3VpyIodffSm95mfd90MGknl4REZGopdAbK2KxvaEgwMqnv6E3+7Bj67RwU6VXREQkain0xgpPlUtroyd0HdsPRQVQWhLYPP62N0SitQGi589fRERE6lDojRWeKr0FuVBRHp61eJJ1MDjtBX5XeiMUetXeICIiErUUemOFpxAZLf284Oip3bst8HliLfSWlzkq3CIiIhJ1FHpjRa6HSm80hV6AXZmBz+Fv6I3EiymqqMVBREQkKin0xgqPld4oeYityu4ghF5/e3oj8QriKgq9IiIiUUmhN1Z4Cr25qvRWi1R7Ayj0ioiIRCmF3ljh6UE2tTf8IJKhVw+ziYiIRCWF3lgRa+0NR/cFPkesbVkGqvSKiIhEKYXeWJGb5f6FC9FW6Q0GPcgmIiIiQaLQGysqyt0Hqvwoq/QGg7+h9/jh4K7DF2pvEBERiUoKvbHEXV9vtD3IFgylxY69b31RkAvFhaFZjzdU6RUREYlKCr2xxF1fb0NsbwDfq72R7OcFhV4REZEo5XPoNcZ0NsbkGWOsMaa503FjjLnXGLPbGFNojPnQGPMzF9f3Nsa8Z4wpMMbsM8Y8ZIxpXGtM2OeKCW5DbwNsb4DYC70FCr0iIiLRyJ9K718BV0lkKnA/8DgwvHLMGmNMx6oBxpgUYA1ggRHAQ8CdwIORnCtmuGtvUKXXIZIPsQHkq6dXREQkGvkUeo0xA4FLgCdqHU/EES7/Yq193lq7BhiDI5BOdhp6I9AUGGWtXW2tnY0jpN5hjEmO4Fyx4USs9Pq6bVmkK71qbxAREYlKXofeyraBWTgqqkdqnf4lkAwsqDpgrc0HlgHDnMYNA1ZZa53LYWk4wuv5EZwrNqjS61mkQ6/aG0RERKKSL5XeG4EmwAsuzp0OlAPbah3PrDznPG6L8wBr7S6gwGlcJOaKDfVVekuKHF8NUayFXlV6RUREopJXodcY0wZ4GLjDWlvqYkgKkGetLa91PAtIMsYkOI1z9XP4rMpzkZqrmjHmBmPMRmPMxsP5xS6mj6D6Kr0NtbUBYi/0ap9eERGRqORtpfdR4DNr7fJQLiYaWGvnWGv7Wmv7tmvWJNLLqam+Sm9DbW0A33t6j+4NzTq8pUqviIhIVIrzNMAYcwYwCRhojGlVeTip8p8tjTHlOCqnzY0xjWtVVVOAAmttSeXnLKCli9ukVJ6rGhPuuWJDvaFXlV4Aykph2xehW4s3igsd64iLj+w6REREpAZvKr2nAfFABo4QmcUPfb17cDzctgVoDPSodW3tvtst1OqlNcak4gjRW5zGhHuu2FBve0MDrvT6Enq3fRHZt7FVUbVXREQk6ngTej8GLqz19XjluUtx7Nv7KZCDYzswAIwxSTj2xV3hNNcK4GJjTAunY+OAQuCDys+RmCs21FfpbYivIK7iS3vDVx+Fbh2+UF+viIhI1PHY3mCtPQKscz5mjOlW+cuPrLV5lcdmAPcbY7JwVFHvwBGqZzldOhu4FVhsjHkc6A48ADxVtfWYtbYoAnPFhvxsqKiARo3qHm+ofKn0/i9KQq8qvSIiIlHHY+j1wQwcYfIeoA2wERhira1+RZa1NssYMwh4HsdeudnA0zjCasTmihkVFY7+3eTWNY835Eqvt6HXWvj6k9CuxVsKvSIiIlHHr9BrrX0FeKXWMYtjl4dHPVy7GbjIw5iwzxUzco/WDb2q9MLOze7fWBdOCr0iIiJRx6fXEEsUyHER7Bpypdfbnt5oaW0A9fSKiIhEIYXeWOOqmqndG6LnITZQpVdERCQKKfTGGlfblmmf3uiq9Cr0ioiIRB2F3lhzolV6vWlvOLQLDu8O/Vq8pdArIiISdRR6Y02uKr11RFNrA6inV0REJAop9MaaE63S603ojabWBlClV0REJAop9Maa2j29FRUNu7JYXODYg9cdhV4RERHxQKE31tSu9OYf9xwKY5m1UJRf//mcY7ArM3zr8UZD/kuIiIhIjFLojTW1Q29Dbm2o4q7F4euPoy/0q9IrIiISdRR6Y03t9oaG/BBbFXehN9oeYgOFXhERkSik0BtrTsRKr7tty6KtnxfCF3o/ezs89xEREWkAFHpjTf5xKC//4XNDfgVxlfoqvUUF8O2/w7sWbxTlhb7l4oMF8OBIyD4c2vuIiIg0EAq9scbamtXd/BO4vWHL51BWGt61eKOiAgpyQzf/t/+BJ66F8jL4ZEno7iMiItKAKPTGIue+3hO50huNrQ1VQtXikHUIpo9wbOUG8NGi0NxHRESkgVHojUXOfb0nQqW3vp7eaHyIrUooQm9pCTw0quYrlzetrftwo4iIiNSh0BuLnEPviVDpddUqUF4OWz4L/1q8FYq9ep/7A3z9Sc1j5WXwyZvBv5eIiEgDo9Abi5wreyfC7g2u2hu++493ryiOlGBXepc8C6v+4fqcWhxEREQ8UuiNRc6V3hNhn15X7Q3R3NoAwQ29X6yG/7uz/vP/fe/E+D4QEREJgEJvLFKlN7ofYoPghd6938Jj46CivP4xZaXw6VvBuZ+IiEgDpdAbixpQpbei9cmeB7kKvbV7W6NNMHp683Ng+uXe9W2rxUFERMQthd5Y1IAqvdmtfuZ5UO32ht1bIftQaBYULIFWeisqYMavYVemd+P/vdoRkkVERMQlhd5Y1IC2LNuV3c3zoNqV3mhvbQAoCDD0/uMe+Pwd78eXFsNnywK7p4iISAOm0BuLqkJvUYFj79YYZePiycxs5Xlg7dAbhofYytt247Nmt1OU+gv/Jgik0vvNRlgw0/fr1OIgIiJSr7hIL0D8UNXeEOOtDRUpJ7P7o8bQwcPA2qH3649DtibbvA2ZzUbz5sJOlBYbmow9mzPZ4PtEgbQa7Nri33UbVzr+rJo29//eIiIiDZQqvbGoqtIb4w+xFSedRGGewTZv7X6gc0/v0X2wf3vQ12ITmrK7y1U888FkFr52EqXFBoD3V7XFNknyfcJA2huO7vXvupIi+Oxt/+8rIiLSgCn0xqKCHMebuGK80pvfqD0AZS06uR/oXOkNcmuDbdSIo6cM56Wvp/CPl08j55ipcT7vuCGr80W+TxxIe8ORPf5fqxYHERERl9TeEKtyjsX8K4izix0V3uIm7Yh3N9A59AbxIbb8rufxzr8HkrnK7d3ZsK03Fzf2sYIaSOg9HEDo3bDC0eud6Ed1WkREpAFTpTdW5R6N+Z0bjmS3ACDftHU/sCj/h1//Lzj9vJ8k3sETLw0i89/uAy/AZ2ubUt6mi283CGSf3kAqvcUFsH65/9eLiIg0UAq9sSo39iu9Bw44qpE5JS3dD6woh+JCR/V0x1cB39fGJfD+8hY+XGDYnXSBbzeJVHsDqMVBRETEBYXeWJUT+5Xe3dsd3TXZuV7sNlCY53gLW0VFwPcta9udinLjeaCTdR93xRofrikrdQR1X5WVBv7ijfXvOB5qExERkWoKvbEqxiu9NimZrMOOb78jR5p6vqAwL2gPsRU07erzNTu/aUxx6tk+3siPFodj+wMP9oV5sGFlYHOIiIg0MAq9sSrnWEzv3lDWMrX61wf2eu6rpSgvaPvzHivt6Nd1m3N8DL3+tDgE8hCbM7U4iIiI1KDQG6tyj8b0Pr2FiT9sU7ZvVyNsIw/fijlHYasfL4lw4cAxD/sC1+O9la2xiT68+MGf0BtoP2+Vz5ZBSXFw5hIREWkAFHpjVW5sV3rzKn7YsaGsxGCT27u/4L/vQ2lwQtzOHf5t51WQazjW6ULvL4hkpbcgB754NzhziYiINAAKvbEqxh9kO1bQqsbnsuYeWg42rAjKfW3jOLZv8X976vVbe3s/2J+e3mBVekEtDiIiIk48hl5jzJXGmE+NMUeNMUXGmK3GmGnGmASnMcYYc68xZrcxptAY86Ex5mcu5uptjHnPGFNgjNlnjHnIGNO41piwzxWTYvxBtsNHa24ZVhTfzv0F274Iyn3L23SrfsWwP9Z/mEh5227eDY5kewPAZ0sdu0GIiIiIV5XeNsD7wO+AYcA/gPuAp5zGTAXuBx4HhgN5wBpjTHX5zhiTAqwBLDACeAi4E3iw1v3COlfMyjka0+0Ne/ck1Picbz302VoblPsWNOsW2ATWsCvxAu/G+hV69/p+TX3ysuHfa4I3n4iISAzzGHqttf9nrZ1mrV1irV1rrX0cR+D9TWUlNRFHuPyLtfZ5a+0aYAyOQDrZaaobgabAKGvtamvtbBwh9Q5jTDJAhOaKTdmHar6eN4ZYY9j9Xc0Wg+NFyWG5d3Z5J8+DPHj/w1Tv9uyNdKUX1OIgIiJSyd+e3qNAVanul0AysKDqpLU2H1iGozJcZRiwylrr3OiYhiO8nh/BuWLTsf2RXoHfbMuOlBTVDI3Hcnx4Q1oADmb5t3ODsz3fNaaoSz/PA33t6a2oCP6/14y3oLwsuHOKiIjEIK9DrzGmsTEmyRgzALgV+Lu11gKnA+XAtlqXZFaeq3I6sMV5gLV2F1DgNC4Sc8WmIP24PxJKWpxc59jhw03Ccu+du5oFZZ6vs7zYs9fXSm/WweD34OYchf+uDe6cIiIiMciXSm9+5ddHwAfAlMrjKUCetba81vgsIMnpgbcUwNV2A1mV5yI1Vw3GmBuMMRuNMRsP52uf01AoiOtQ59iBPV68oCJAtlEjvssMzn3eX9UKm+ShJcPX0Bvs1oYqq/4RmnlFRERiiC+h95fAeTgeGBsBPB+SFUWYtXaOtbavtbZvu2bhqT6eaHLK22ug8QAAIABJREFU2tQ5dnCPwcaFNvhWtO5CUb7/Ozc4K8wzHO3gYc9eX0NvsPborW1dGqwPzpZvIiIiscrr0Gut/be19mNr7VM42hv+YIw5FUfltHnt7cJwVFoLrLUllZ+zgJYupk6pPFc1JtxzSZgdzalbIbUVBtsytJtqFDY/JajzfZbZy/0AX3t6Q1XpBXjmBsj3Y99gERGRBsLfB9n+XfnPU3D01jYGetQaU7vvdgu1emmNMalAktO4SMwlYXbwkOu+2pJmge+s4E62De78X3yUSHn77vUP8LXSezSI25XVdmQPzPlT6OYXERGJcv6G3nMr//k98CmQg2M7MACMMUk49sV1/pnqCuBiY4zzY/rjgEIcPcJEaC4Jsz07XbcxFMW1dXk8WA4dr9tWEajv4y+o/2S0tDdUWfGi9u0VEZETlsf3sRpjVuJ4EcTXOHZDOBdHX2+6tfa7yjEzgPuNMVk4qqh34AjUs5ymmo2jLWKxMeZxoDvwAPBU1dZj1tqiCMwlYWTjm7B/l+u/a+WWp9DK5Zng2LW7edDnXLvuZE7t2QhTUVH3ZLQ8yObs6ethzlfQNPh/FiIiItHMY+gFNgDXAN2AMmA7cA+O4FllBo4weQ+ON7htBIZYaw9WDbDWZhljBuF4AG4Zjt0XnsYRVonUXBJe5Skng3X9MFlOYWhfUPHtZpcbdgRk345GFA7uT9LOT+qeLC5w7JHb2Jv/mRGe0HtwB7w8FSY3yOdQRURE6uXxv8bW2vtxvMrX3RgLPFr55W7cZuCiaJtLwqc4qXO9545mJYXsvhWtTyY/Jzg7N9T21bFfcA4uQi84Hh5L9vKFGMF8BbE7y/4GA8fCTwaG534iIiJRwN+eXhG/5Jt29Z47dDAxZPctatEtZHOvW9Wy/tcSe9vicPwIlBQFb1HuWAtPXQfFheG5n4iISBRQ6JWwyipKqffcvt21d5cLnmxTf4U5UEX5BlrU85BcgZehN1xV3ir7voW594X3niIiIhGk0CthdSSr/r7drEONsAlNQ3LfQzmh3RmirFndt8wB3u+NG45+3trefBY2Z4T/viIiIhGg0CthtX+/+xaGilYnheS+e/a28DwoACVN6qn0etveEOrtylypqIAnJ4WvrUJERCSCFHolrHZvd/+q4eKm9VRMA/TtluDv3OCs0NTTtuFt6I1EpRdg9xb41wORubeIiEgYKfRK2NhmKeQcc7+DQmGj4L9AoqJle44f8f1bPbF1Io2beNdnnFdWzw7D3r6KOFKhF2DhE/DNxsjdX0REJAwUeiVsSlulehyTW+bl9l4+KG55qtdj45vF03xYc7b9aRvTb5mOGe3dNmc5Ba5frRzV7Q1VKsrhiWuhtCRyaxAREQkxL3fNFwlcYUJHj2Oy84P/prCcRu53bmgU34jm5zZny8+2sKDlArJNdvW553s8z22tbqM4u9jtHFnH63kAz+vdGyIYegF2/A+WvgCj/xjZdYiIiISIQq+ETW6F5x0UQvGCisP5LvYGNtDy7JbsPGsn6e3S2W/2u7z2QKMD5I3OI/5l973IR44k4vIdyt5Weo+GecsyV/77vkKviIg0WAq9EjbH8uvpe3Vy6EATCPKzbHv2190m7d373uXTuE+9uv65k5/j/pPup2BfQb1jDu5rVE/o9aKnNz8HCnK9WktIbV0f6RWIiIiEjHp6JWwOH/HcurBvZ/BfULF9a5ManxPbJnodeAFyTS57RrpvPziyvxG2sYu/Q3rT3hDp1oYq2YfgwI5Ir0JERCQkFHolbPbu9rxtWN5xg23muSLsLdu8DYf31fw2b/KjJvWMrt/f2v6NFqfVv9evrTDYZBclam/aGyL5EFttqvaKiEgDpdArYWEbNWLP995VccuSOwXtvsUp3escK+hSf5tCfcpMGV8N/8r9mGYueoe9Cb3RUukF2PJ5pFcgIiISEgq94hPb3L8txWyrkygt9m77r5Im7f26hyu5cSfXObanvX8h85XkV2j585b1ni+Od7HHsDf79EZT6FWlV0REGiiFXvFaSeefMP/bm/y7trn7bcOc5ZvgvaDiSEHdAP1l8y/9nm/dkHX1nivAxVvZvKr0RsHODVW2/RvKyyK9ChERkaBT6BWvlHU4jTkrRrLtqzhKO/fx+fr8xt5Xb3NLg9fTu/dgzZ0bGic0ZmO8/28fe6vpW7Qc6Lram1tSd5cICnLAWveTRlOlt7jAsWeviIhIA6PQKx5VtD6ZuR9P4OhBR3vC3kY/93mO46We9+itkpVbz9vN/LD9m8Qan5ud1oxi4/5FE568cd4bmEZ1WzVy8l2su6ICCvPcTxhNoRdgi1ocRESk4VHoFbdscjvmfXUt+3b88K3y7/9536pQ5WiO650PknvWrY4eORqcF1TYpGT219oCrfzU8oDn/Tj+Y5oNqxtwj2XX91Y2D3290bR7A+hhNhERaZAUeqVeNqklb+y8nu821wyOX22Ip6KVbzssHDzounpbcFbdnRQO7vO8tZk3SlqfWufY4Y6HgzL3Kz9/hcYJNf9cDh+s561t7vp6iwsh91hQ1hQ0ephNREQaIIVecckmNGVF1h/4eqOLAGoNR1P6+TTfnh2uA+HnXepWFfftbPT/7N13nFTV2cDx35letjfYXoAFFhVUWLto7GiMLSaaGI0xRqMpYrAkJq+v0dcUTaJRo5jYE2M3sVdsKFIEqUtfdmHZ3nfazsx9/7hsmZ07ZWdntnm++fBh55Zzz+ISnjnznOdBEdFVegin21gYdGxb+rYRjwuwUb8R/TmBQW/DvhAl2cIFveMttQGgZnPklAxJkiRJmmBk0CsFUQxGPvRdy6qPLCGv2VwbXP825HgmKw17g3/UbLk2PjZ/HHTc4xKQHH0OcCjNruDNcyvMK0Y8bp+/zfobpuSBNwUdLToUk0aKQ7igd7ylNoCah7wt9s1+kiRJkjQeyaBXCqDodKy0XsOHb4bfTLbioyTtAE+DLz14xRXAWGKkXbRjmxqcw9ubPPIGFfsbA6tA2Avs1OvqRzxun1pdLa7zXQHH/KlTgy8Ml9M7nsqVDSZTHCRJkqRJRga9UoCNmVfx5ouRS4a5egQ9edGlOLiseZrH3XlqFQVjUXDqg8s08gYVu3cEBuX6GdF1hBuOe4vvxZoz8JxeyzC7so3H9AaQFRwkSZKkSUcGvVK/nfnf58V/RR9s7uyaHdV13UIjEARaslsA6M3tDTrnUGLr/NZHMduo2Rn4491ZGEV3tGFqF+00ntfY/9pl0GisMSGDXlnBQZIkSZpcZNAroaRksz7rxzz1WPGw7lvxaVZUG87aHBqdyoDq5GoAOrKCg8IOt0ajh2HozZwGSuDcqrOqRzRmKPdPuR+hV5/l8Gs0rgiX3jAec3pBDcZb9o/1LCRJkiQpbmTQ+xWmWJPZXXAZd7/zY156evjpBPU1OnrzIndna27TrtG7wbIBgLrUuqBzbZ1Jw57PYD2WoqBja+1rRzRmKC7hwpar5iV3ujW+14m40gtytVeSJEmaVGTQ+xWkGEzsL7mQ+1dexxOPltDTGXt5sGi6s9XVmYOOGe1Gtuq2ArDduj3ofFNT6MoR0WhxTwl4bUo2sV63fkRjhmPINQDQ0a2xAXCiBr1yM5skSZI0iRjGegLS6FF0OtqKF/Hy+4dS+0Z8NnV9sSGf0gj73mp3Bm9Us5ZaUYQCwHrDek4WJ4MycL5+nwlmxD6v/U2Bk7LOHHheIviy1U5vra0WGFraOFTQ6+2F9kbtc+OBDHolSZKkSUSu9H5FdJecwLONN/PXpfOp3RG/KgYbVxnxp2tXZwBQkjM1V5KVwoEAtFN0BpUtq68VKLrY51m9K3A8d7E75rGi4chUO8s11mu8jwyV09tSB0riAvER27pqfM9PkiRJkoZBBr2TnJKSzevum7j74ROoWhuiTe4ItaQdEfJcb6p2jd6OnMDVz6Fly/w+gTLMVsd9FKOZ6m2BAfP+qYndlNWarLYS1uzKFmqldzynNoAarNdWjfUsJEmSJCkuZNA7yXVmHha2s1o8bK6dFvKcw6jRrAGoSw/cvKZVtqzXPiXoWDS8WWX4fYGry5tSNsU0VrTq7Or34+wWKPYh+R6hgt7xWrlhMFmvV5IkSZokZNA7yTV6ChL+jBUf2UN2Z+v0abcT3mrfGvC6PbM96BqnQbu+bySOIZUbhF7wuSmxlQh2m3f3f+1LGhKsT9SVXpAVHCRJkqRJQwa9k1xN3ciaPETD1SPoydfuztbaHVy3VugE6wzrAo5plS3r8WvX942ktTdwdTmpLIku0RXTWNHaqh8I4j2WIYF+qJzeibDSKzezSZIkSZOEDHonua0bbZEvioOdnRWaxxubg0t42QvsQUGoVtmyDqdGo4co1LcOCfSnxzTMsHSKTixZahqJSz/k+b1u8GhspJsIK72714PHNdazkCRJkqQRk0HvJOZPy6WpLvYavMPx2fJMze5s+2qG1u8CfUnwZq/1hvUw5PbWDo2at1Go3h0Y6LfmtcY0znCZ89R6xN1ejWBdK8WhZd+Inqcka6eOxJW3F3YkpqmHJEmSJI2miEGvEOKbQoj/CiH2CSG6hRBrhBAXaVz3QyHEdiGE68A1J2lcky+EeEkI0SWEaBZC3CeECFqKHO2xJitH2qxRe1ZDrY7evEMCjik6PfuqgwNc51Rn0LEu0dXf1axPY2NwU4tIFL2BXVWBZcN2ZOwY9jixUKao5b26nBrd5BwaQe8I0hv8GQUsXf1jenO1V9jjSqY4SJIkSZNANCu9i4Fu4DrgbGAZ8C8hxE/6LjgQBD8IPAGcAWwCXhVCHDToGiPwFlAMfBv4GfBNYOngh432WJNZi0+7XFii7BOHBrz2p+fj6w1e/W3KatK8f2jZsvq9wy+x5ssswesJfOYq66phjxMLV4aaBtDWpZFS0jMkr9fvh9bYyqgpFjsv7LiU+hod71SfHdMYwyI3s0mSJEmTQDQd2b6uKErzoNfvCyHyUIPhvx44divwuKIovwUQQnwIHArcBHz3wDUXALOB6Yqi7D5wXS/wbyHE/yqKsn2Mxpq0aptG4ePvQdZsyKN00N4zT1K+5nW7kndpHh9atqxpPyiHWhDDyCl12EsCXluzrezSaT8v3trT2rFho6XZDEMXe4emN7TVg8877GcoQrCcH7F5jfqGYNVHFo66chHpu1+PcdZRkCu9kiRJ0iQQcaV3SMDbZy2QByCEKAPKgWcH3eMHnkNdXe1zBrCqL0g94GXAA5w+hmNNWtu2xJYTG6tNq0340wcC3R5djuZ1X5q+1DweVLZMEfhTQ3d70xzDG9jQwlQenFOcKA32BgAa92u8lxwa9MaY2rAr7zLeeyUl4Ngzb8xHsSbHNF5U6nZCZ0vixpckSZKkURDrRrajgG0Hvu5LHB3aumkLkCGEyB50XcA1iqJ4gJ2DxhiLsSYlxZ4e13bD0Woe1J2t3ZMZdN6cZqZaX615776U4I1dHtvwGlTUtwVWTugp7hnW/SNRY60BoGGfDkU35K/W0JzeGCo3tJct4qnHioOON9Tq2Jr67WGPNyyySYUkSZI0wQ076D2wEewc4O4Dh/o+0B7aXaBtyPl0jWv6rksfcu1ojhVACHGlEGK1EGJ1U49GmakJwpU1epvYBttcW9b/dUtHStB5S2no7nDbbcFly5z64MA5FMWWwsrPAisn1GbXRn3/YOmWdH4y/QIOzz4k8sUHbNOr7wN9vSK4ssLQnN7m4VVu8BTM48EnFoQ8//y/8/BOnTmsMYdFpjhIkiRJE9ywgl4hRAnwL+A/iqI8loD5jDlFUZYqijJfUZT52fbhVw8YL1pFyZg8d8UHdhSzupGrvj64S5s3P3Qe6wb9hqCyZd3e6BtU7Ey7gJb6wB/p9Unro74f4IS8I3hq9rXU5V7Nvd5yXkg7k0xrdIH3Pt0+jHY119ZnH5LaMTS9YRgrvf70fP7xztm4naHLz/l6Be/vOyfqMYdNbmaTJEmSJriog14hRAbwBrAH+M6gU30rp0OLk6YPOd+mcU3fdW1Drh3NsSalulbtfNpEczsF3Xlqd7ba3cHVF9pztBboVV2iC3teYB5yuyO6XFV/RgHPPxtYrcJgMbDasDrivVPsOdxYfjHbZt7EMtNJfMeZgkWtPkZxr+CfJZeiE9H9VbEWqIG+2zQke2ZoekOUOb2K2caLuy6lcW/k53/2vpWO0tOiGnfYto1OBQxJkiRJSpSo/iU/UP/2VcAEnKUoimPQ6b6c2aGfp88CWhVFaRp0XcA1QggTUDZojLEYa1LasT2BG5si2NkxG8WSFLTqCrA3LXywZygK3ATW0hpdR7k13vODVkLt5XZ6Ra/m9Tqh44yC43mh4mfU5lzB7zwlzHBr/3U4zWnhN+XRFfsQU9Q5OEVa4IkYVnoVIfhUdxWbVke/Ge/Zt49AsSRgA2NHM+wfnSoYkiRJkpQI0TSnMKBWPJgBnK4oSuPg84qi7ELd1PbNQffoDrx+Y9ClbwALhBCDd+KcDZiBN8dwrElHMdvYsWn0N7H1+Wx5Jt70Is1zW2xbwt7bOzUwSG1sCJ0D3MeTP5fXX0wLOt5bqh3wAmwov4HXDcdynsOOUYn4CH7jKuCMguMjzyXLA0B375B8ZsfQnN7IQW91/qW8+9/gvOhw6qp17MhM0KY2meIgSZIkTWDRrPQ+ACwCfgtkCiGOHPSrL+n1VuD7QohbhBAnAo+gBsm/GzTO86irry8KIRYdaBxxH/CvQXV1x2KscUMx2+gsPXnE5ac82bPw+0an/bCWxr06avRHBh3XGXR8qdcuV9anLTMw66SuJnLw/t7uM0DRaIKRq72YX54+jYoQq7qhCAFPWY6hJEU7mO/TldYFQIdjSKHeoJXe8BvZ2ktP54lHS4Y1xz7PPV2AL2daTPeGJSs4SJIkSRNYNP/yn3rg93uAz4b8ygVQFOVp4CrgMtSV1kNQ0yA29g2iKEovag3dWtTaufcBLwBXDn7YaI811hR7Gq2lZ/KJeTF/WLaEPy89lu6plSMas8NYGqfZxe6NNwqCjtmL7LhF+IoYQ8uWdbaKsG8CukpPYuWH2qvBValDq9WpFmYcHHYOoWR4BS/kXYRZH3qDY1OKGmi3tQ/ZxDc46G1vgt7Qfw6+nGk89M8jQp6PpNct+KDpvJjvD0lWcJAkSZImsIgd2RRFKYlmIEVRHgYejnDNXtRyZ+NqrNHmT82hJeMY1u+ezorXbUFtc6u7Z3Iw78U8/v7OqSOd4ohp5fOK4sirz9ut2ymjLOCYLzUPg3Nr0LWKwchLHx8VcqwVlhWaxxeaC8CheSqiw1x67iu/nB9u+Zvm+VpLLbnk0txsgsElgwenN7SEX+Vtth+Kq2dkK/WfvG2n8sqTSN4d+89RkB1fqF3k9NE0cpQkSZKk8SXW5hTSMCkGI/tLLuR1903c/uLVPLB0Lp+8Yw8KeAHWrI6+Nq2WXbu1CluMPUdu5EhzvWF9UNkyt1m7EkVD4Tns3qIdgCUVJdEktNMbFvaOLH3kCmc6l5edrXluh2kHAI11Q+Y1eKU3QuWGuvYoG3JEiIuffe9oFFNw2biYeVywa3gl4CRJkiRpvJBB7yhQjGY+1v2UpQ9XsOojC4o/fLSyZ5seX3ZZ2GtCPktvoOrL4FJh40F9Rn3Ea3pED/b8wOoDDl1W0HWKPY1nX54dchzdDO0f7bLUEgp6R57vfL9yEIdmHxR0fIfYgc6oo7leoBgGVV0YHPRG2MS2c2fwpryhTKkm1ty4huSy0AH83p16qnO+FXGsYZGb2SRJkqQJSga9CaaYrLzv/SnLXhve6mKz/bCYnufNKcfjGrtNbOHsTNoZ1XWGwsBV0q7e4CBwe8oFtDWG/vHtKOjQPH58Zmz5vENZFHgh/eukWwKbZ/iFH1uuDRSBkjJohdrZDX6/+nWYlV7FaGbrhvDpAwaLgRVXr+AVyyu8etGr6E2hN/s98+9ifFklEb+fqCUir3fjJ/EfU5IkSZKGkEFvAikWO287f8onbw+/bmrV3pKYntllTcCu/ThZZ14X1XWeXE/A6/buwEoIvswiXng2P+wY1VnVmscXmsNXXxiOUo/gybLLEEPyDPS5ahDaaxuSltGX1xtmpbc3Z5ZmyksfoRdsvWYr/7X8F4BlxmV0X9Yd8nq3U/Df3RfjzwjeWBiTeAe9nS2w5ER454n4jitJkiRJQ8igN0EUWwqvtf2UFe/HllO58hN74MfjUWroyYvpeYlmybSwX+yP6tqhZcuaWwP/DFe5zou4mr3Gtkbz+ELf8OreRnKmw8qvyr8TcMyX7QPAbRySm92X4hAm6G03hXnTIqDpx008mfxkwOHf5/2e1BNC53GvX2Hi3nd/QHvp6aHHjlZtFfR0Rr4uWps+VTfH3XUZvKK9OVCSJEmS4kEGvQmg2NN5ueFa1nwSurRVJI4ugSv/8GHfV12bHvmiMWAu0/6z+FpecPWFoWXLGuoGgn93wWG89XL4nFdTqomN+uCqdIXJ+ZSGWUWN1a2eIqyGgcC8O0NdeXUoIbqyhanRG67yhusKF/dl3qd57u6Fd6tpFSF0tAjuWXoka1J/MrI60H4/bIvc2jlqm5ervysK/PXH8Owf4je2JEmSJA0ig944U1KyeL72x6xfMfxV2qH29lYM79lCsGV97IF2IvXma3dHuzYtuCbxduv2gNeDG1S8s/20iM+yztReXV+YNS/ivbHQAzPTBjYetqa0AtDlGbKqHMVK785d2gG97js6fpcfuqfKXrGXT7/3KUIXPqh/9flMntj6c9wFh4a9LqxNy2O/d6ih+bx/vxEevSV+40uSJEnSATLojSN/2lT+vf0qNq+JT/WEdRujLF3V9/ysUrraEruJTW+Orb1xa3Zr0LG8pFzOdNox6QPfIKzXrw8I3lw9AiU5i87SU6NaPXcVuzSPL7TEL593qIqkgbH32dSV3I6eIbncjk418HVq5+AqBpNm5Q3buTZ+M+M3EefwsvVllO9E7qlcvVXPH586mz2F30PRxfDf88tlw79Hi8etvWr89B3wt5+rq7+SJEmSFCcy6I0Tf3o+T228gm3r41e4f/MXRvyp0Qe+3Ukz4vZsLdZsK58s/gSdcfg/NjWpNUHHFmTMxqTA3MzA0mMO4cCWH/hRvSe1kJc+jK5LWV1Onebxhf7E1S+uGFRLeLdpNwCtrUM6xfV0hK3c4M0pp9cd+KYl5ZQUbjrkpqjncXvZ7aQuiPx9+noFjz1SxuvOJfgzC6MeH4Atn6kB60htXxO6M91L98CfrhioeCFJkiRJIySD3jA63Ok81/orPjJdz+acq9hXchGdpafgyZ+LkjJQO9aXWcTjay8P2SghZoqgPXNB1Jc3ecJXNBgJvVnPsiuW8Zb5LZIWJkW+YYjNls1Bxypt6upoZcr0oHNDy5Z9sOdUqrdGtyq5KXVT0LFc+1RmuBP3416hG0hL2KrfCgKaG4ekuPR0hE1t6DAH/jmkHp3KLUffgl9EH/h5hZelZyzFnB5dmsvqjyzc8/bltJctivoZeFxQpd3tblg2RShV9tYjcOdF4NVOjZEkSZKk4ZD9RMPw+wSb1xjZjBFIBqYCM/vPW5MU8kt8tG3QabbdjYcdTdMJznrVVrN/ZJ3cwtl79V7eNL8JwIpDV1DxbvT5xnqzXnNj2QKh9umtNE7l/iHnhpYti7YKhs6g43NjcAOFhdmJyeftU+EbmF+P6MGabaV+nwNyB10UYaW3rmug8kbq3FRuO+U2HGL4/ZK36LZQ9f0qSv9UGtX1na2Cex6q5Ps/yKCo5qnoHrJuGRyycNhzCxBNbvCHz4LLAb9+DkyWyNdLkiRJUghypXcEnN2CHRsNCQt4AVZ+moIiosvTrdoQevf+iHwPHkp7qP/lc7bnSCqOfrXXXmLHK7wBxwSCBR51NbLSFzxWW0Zb0LFoJE1Lokf0BB0/3loc03jRmu7RYdYPrK6a8kx0tQkUy6DvzdEZdqW3r320vcDO3efcTZuI7c8A4PGUx7FcMLwg8c13y1B0Uf4sxyOvd/On0V33+atwy5khc6ElSZIkKRoy6B3nWup19OZF7iLmT8ulqS7+m9iSz0jm1rJbA44pQqH9pPboB9HYPzYjvYw0nzrfmW49qebAPNS9KeFb9Ybin6adCrDQn9hSbnqgfFAFB2WKugnLnzyoQUVPR8hyZWr7aDUdwn+En70itu9/sN/O+S0pFdHXJd6/R4ez6OjoLq5aAW5njDMDaqqgozn669e9DzedAl2xvxGQJEmSvtpk0DsB1OsPiXiNI21mxGuGK/WwVH5TqV014J8F/0RniO7Hp2tqV9CxBakD8xUC5mfOCTi/zbptGDMd0JoXXCUi25ZFRQLzeftUJA9E984MNSD0WIcGvdrBrDenHLdTfRNQm1cbl/n0iB6ePu9pjLboq4lsbJkf3YW9nuhXarXEUvZsywr4xUJorY/9uZIkSdJXlgx6J4CN2yO3kG3xx7ccl73Qzj1n3aOZKgCwW7ebpBOiS3HYnxHcia3SErjprjKpJOD1Rv3GiDVntWzJ3BJ07PjsEdSkHYYK80CljbYUdUXSpc8YuCBM0NtpGdjEtjolfs0fVhlWsfXqrdimRJf6suytVBRblKvD60aQ4rA5xlq/uzfA4uOgYU/sz5YkSZK+kmTQOwF88Zk5YiCytzEr7PnhMCWbePl7L1Otqw573eeHBm8Y07Ldvj3oWCWBTRgq9dkBrx3Cgb1gSJ3bCJJKk/iP5T9BxxfaotvQNVIV+oHvqSGpAQDH4DJpYXJ663vUNwGmZBMrDSvjOq8nkp/gzqvuhEvBaA+/6uvqEbRMOTG6gUeS1zu0KcVw1O2A645VUyQkSZIkKUoy6J0AfL2C7inhS5dt2zK8ADEUoRdsuGoDy42RV+Kesz1HUlHk1d61xrUBr406I4e6A4OvI3qDVyL1hcNrnNB8crNmea+Fyui0Zh5cwaHaUg34WGevAAAgAElEQVRAp3tQy9/W/SFzUnftVgNm60HWYZUoi1abaOPW0lt55PpHsJ1rQ+hDr6KvrJod8lyAbavAqf1JQPjJNMK+4DdCw9K8F64/HnasjXytJEmSJCGD3gljd1fonF3Fnk7Njtg6pQ3VfUU3T9ufjupav/DTcVJH2GtsuTZaRWCe7cGZszAPabaV6xXkJ+UFHPNMDSxbFo7RZuTRvEeDjmdYMjjYGZ8/m0hmePQYdWowv82g5iS3dw4K5vfv0rxP0enZfKATW3dZYisU7NTt5Ia5N/DmTW+SeoJ2E4tVH5vxZZVEHszbG7nWrpaR5AIP1tEES04c2aqxJEmS9JUhg94JYs3q0DV4XVmz4vIMywUW/pj7x2Hd86/Cf4Xd0GYsCf44XasZBUBlRuAKY2tm8Ka0kM85zUiDaAg6flzOPKKs+DZiRgWmp6mpFA2iAVOKKbArm9+neZ8vZwauHvXPcOeUnQmfJ8Bnxs+47oTr+PLGL0k9ZEjwqwhqrVHW4I0lxSGWTWyh9HTAzafB6rfiN6YkSZI0Kcmgd4Ko2a7HlzNN81ybGHkN2pTTUvj1nF8P+76dup1hO7S584PbzFaaczWuHOjQ1mc4Zcv+UxGcywtwvK1M83iizEke+G9hybfQWB+5ckKndeBNwKf2OK2CRukl60ssPncx+3++PyCH+oNPiqKrD/3lB8N/aCyrw+G4HfA/Z8NHz8d3XEmSJGlSkUHvBNJkP0zzeF3rFM3j0dAZdTivdLL4qMX0itjava48LPTGq5bMlqBjC3zJGldCpcgIeL3VvDWq56cekRoyB3khGZrHE6XCMrX/azFFUF8b+a9YvUPdxGbLtbFNH1uptpFQhMJDaQ/x+cUDGxP3bNPjLoyiBfb2NeAILkkXkscFO76IYZYR9Hrg/74NbwWnuEiSJEkSyKB3Qqmq1V7R3b49+u5og9kL7Cz/xXJ+n/f7kUyLZ23PYi/U3khXnVId8DrJlESFWzvHdr7bhE4M/EhuNGwMu+Gqz/qj12seTzGnMM81up22K/QDm+bcWW48LoGSFD7w3r1HvcdQMby5Ds2BHqkXrC+QPG3gDUlVdxQNsH1e2Phx9A/ZukoNUBPB74M//QDuuQp6OhPzDEmSJGnCkkHvBLLqEzuKwRRwTDFZ2bFp+IFd6sJU/nL5X3jLPPJcSL/w03mSdpCxwbIh4PXhmXNC/tCl+AUz0wc+6ncJV8SyZfZ8O0+lPKV57tjsQxmdLWwDBldw6ExT/0y8STmhLkfR6di8Tm1f3FIcvCoeytfyjqIq51KWlF8U40w15iIUGk9q7H/9/tuZKCZrmDsOGE693kRvOlMUeO0huGI2LH85sc+SJEmSJhQZ9E4gji6BK//wgGOenFn4fdHv1BJ6gfdyL4tPWMw+nXZL3Fg8XfR00Kqs0W5kqy4wRaEyOXyObWVaecDrSGXLOk7tCJmWsTBJOwc6kWZ69OiFOufGJDWA9FiyQ17vy56Oo0v9c9uSFdxYQ8vZhSfyuvlEkvyCP3hK+fH080Y46wGP5T7W38Gtq03QURBFzd7hbGaLtSnFcLXUwf+eC7edDy3BzVEkSZKkrx4Z9E4wtb2B7Xo7jNFv1LJNtfHFL77g9qLbUYQS+YZh2KHbQfLCwFxda6k16DkLDKEDQIBKc+BH9u4pwRvh+ujNeh4vfDzk+YXEr2FHtEwKTEsrAaDWqrYTdonQdYK7bAdWtgV8ZP0o4vjfLT2TF/RHBZR8u693FpeVfT3mOQ/WIBownjKw+W7Nrjlhrj5g51robo98naLEr1xZtD55EX5YAa8tVZ8vSZIkfWXJoHeCWbchcNPa/s7oNrGlHpXKA1c+wH+s2lUO4mHVYasCXiuFwUFGZW/4dIVKJbB8VlumdjMHAOspVmp1tZrnkkxJHD7K+bx9Kg5UcNhh3AFAj1e7Hi5Ag1NtMZ08PZlm0Rx23Gumn88T/rkM/a6EgL/7D+bCktNin/Qgbxz0Rv/Xn75nw5+mXW2jn98PGyIH7OzZHLI5R0J1t8M9P4JfnAB7R3+joCRJkjQ+yKB3gtnyhRF/2kCFgL5OXiEJ4BK4/tTrI7YVHqln7M8E5OB25AQ2rsixZVPcGz4VY67biFlv7n9dm6Id1AK8ecibIc8dnT0vKDgcLRUWNUjcJXahN+npcGlXqwDYXaNucvPPCt+F7Zfl3+E+78yQNYf1wFPicL5eGGUL4TCWGZeROk8N1P1+QV1qFGNGk9cbz/q8sdjwEfzoEPjXHWpjDUmSJOkrRQa9E1B7hlpKStEbqPoyfB3Y1IWp3Drt1oS0th3KL/x0nTxQvqouvS7g/ILMiohjGBWYlzVw3Vardtmy1HmpvGt6N+Q4C5O1G2CMhr4KDopQsOZbae/Q3gymCNG/iW1/Qei80z/M+gF3eCLXYjYq8JzhKE7JPyaGWQfaftxAm+CPV0aRQhNNXu9YB70AvW547Ba4dj7UVI31bCRJkqRRJIPeCWh7o7pBy5szA48r/Mpp3cy6sOfjbfCGtq32IZvYbNE10ahMHtiAtlGnXbZs68LwNXyPF+FzhxNpjjKw2q2fqqe52ax5nT+7jO4O9Xtbl7Yu6LxO6Fg6+2qWuKKvw2xW4GXzQo6dOn+Ysw70eMbjWDLVbnLb1hvw5M8Nf8Pu9dAZoYNevJtSjMSu9fDTI2DFq2M9E0mSJGmUyKB3Avp8eQqKTkeXNXJ1gmXZMbSJHYHtuu0kH5+M0AnWGQIDuUpddBvLKk0D6Rtu4Q6qAWzNtvJY2mMh77carFS6IndCS5RZbn1/vWFvtpfG/dqJFl32GYC6IW+5KXAV1Kgz8q/Z1/JDZ+hNcKHY/PCa7RQW5EQIVMPoET14T/X2v97ee1T4GxQF1n8Y+nxrPezfFfN8EsLRCbd+Q013kCRJkiY9GfROQG2NOnpzD6ahJ3xzAnuhndWG1aM0qwGrD1uNvcBOlwjs1LWg1xLV/ZW+wGYbQ8uWuc5w4RKukPcfmTMX0xhu1LcoUJqirmp3Z3TTuE+g6IJLrzW41E1sSXOScIuBKhVWg5WXZ17LtxyxNR0BtebxmymLODhzdsxjPDv9WTUnHHjvnZygGtFBwqU4jIfUBi1+v5rucPuF4OwZ69lIkiRJCSSD3glqv34u1bXhO30ph49N5Pds0rP4Dw/MIS5LLSHTG+KGIWa4dKRbBlY4XVMGAlydQcdTJdrNKPosTC4Pe340VKSUANCS3ILfJ1BSgtMtqvdmAuCc7gw4fnHxqSxyRtEUIoIMr+Dd9HMCGn4Mx3r9elKPVTe0tTXq6Co8PvwN4YLeRDelGKmPnoPrjoH66rGeiSRJkpQgMuidoDZsy2PLeu1c0T5VZWOzUccrvCw9eGnAscr0WVHfLwTMH7TpbXDZsqSTktiu2651W7+FutAd0EZLhVVN0dhnVRuAeO2BebnqJjZ15XtP7p6Ac2eYSuI2jxyvYHX6N3l49tUcOeXQYd+/unLgk4Iv9x0S/uI9m6C9SfvcaDWlGIldX8JPFgyvw5wkSZI0Ycigd4Ja87GFrrbQm9iEXvB60utxf+5ZhSdEdd0O3Y6A15XWgmE9p9Je0v/14LJly+aFD0jMejNHuiJ8DD8KKvTqKvwus5rH6jZmBpz3Z5XQ2ar+91uZvLL/uFFn5BS3La5zSfILrnCm85n1DDbOvJGfT7+QTGtm5BuBp5Oe7s+p/uidZJTkMHnZigLrPwg+7nLAjrUxzHwMdDTDzafCS/eO9UwkSZKkOJNB7ySVMjeFel193MZLNiXzQsXPeUV/NEfEsGK4gOFtyKrUD6QDbDWrlRpSKlJ4xfpK+PtyDsEyDhpvVShq4LpVtxUEOEVgPeXuJDUFw5xuZq1hICA8ZsphpPijbys9XHPcev7snU7d1B/xzOyfckr+MQhCP88v/LSfonZb83oEDVkRavZqrZJuXQm+0LktijV0HeMx4fPC334Gd18OntAdASVJkqSJJaqgVwgxXQjxkBBivRDCJ4T4QOMaIYT4pRCiVgjhFEJ8JISYp3FdhRDiPSGEQwhRJ4S4TQihH+uxJpuOgzoiXxSl2RnlrCy9hvMcaiD3u4yThnW/QWfgMPfwqilUegdyWjfpN6Ez6Nhz4p4wd6gWJs8c1nMSZbbHiEDgEi5sU2109wZ2ZWt0qyvfloMCN/ctSo2i7W8cmBS40JnE28aF7J5xE7+ZeQl5Sdqd154oeAK9Sf1r9em6GeEH1srrDZPP60+dwj2fXsfe4osjb5QbbW89Cr9YCNWbxnomkiRJUhxEu9I7B1gEbAVC9fG8Cfg18Hvg60A38K4Qor/+lBAiHXgXUIBvALcB1wP/O5ZjTUYr81ZGvigK5xefwufpFzDLPfCjcoLTxKn5x0Y9xpyMcmzD7I0xtVdQmJwPqGXLkuck82jWoxHvO10foWXuKLH7oTilEABjnpGOnsCya9X71PSCjtLANyeLlNGvL1zcK/hfdyGvFlyiuepbq6vFerL6JmTDShPeqWHeWNRWQcuQRhthKjfUp59AR7OOf/y9nCd330h36cg7ysVV1edw9Tz423XQ0znWs5EkSZJGINqg9xVFUQoVRfkmELTsIYSwoAaXdyqKcp+iKO8C30QNSK8ddOlVgBU4T1GUdxRFeRA1SF0shEgZw7EmFVOqiXfM74xoDL3Q8/tZl/O8WECyL/j8ncnhPxYfbEFqhNXBECozBjazfXLaJ3SK8EHHd0oXcYxz7OrzDtVXwcGf46e1PbAaw5Yv1dfbsgfeQxYlFzDHHVzabLQc6jLwndIzNM+9f8j7/V/vIEIVh8F5vX4/bPks5KWfrh2oNb27Ss/dSxfynliCd0psPzMJ4fPCS3+By8vh7cfV3GVJkiRpwokq6FUUJdI63dFACvDsoHt6gFeAwf+KngG8pSjK4Ojl36jB68IxHGtSsSyw0Ct6Y74/25bF27N+zg2u0Ivhh7n0XFB8SlTjVZrC1xMOeZ+1sP/rF2wvhL0225bFPfrxlbVSYVW/b0e6g+amgY/ufZlFtDWpbxiWJw2sgp6Rs2B0J6jhDsNczPrgqiCvW14nZU4KAO+8lYtiDFM5ZHBeb/VG6NFOtfFOncmm1cEpDZ+8bed3z1zMlqlXothSNe4cI20NcNdlcN2xE2djniRJktQvXhvZZgE+YGgtqS0Hzg2+LqCOlqIoNYBj0HVjMdakUlteG/miEBbkzGVNwQ/5mjN8OTSA2y2HYdBpdxsbrNKfEtNcKkX4OsSD3Vt8UdR1gEdLhUGdf3tqOw17B1Zwe1LU9AB7oZ09YiBPeZEpujbNiVTUK/hJ2bma52oW1gDQ2qijo+BroQcZvNIbJrVhty50ioyvV/Dsk3nc//nPaCo9F0U3jvbcbv4Urp0P914dufWyJEmSNG7E61+SdKBbUZShH4S3ATYhhGnQde0a97cdODdWY/UTQlwphFgthFjd6Z6YOXzvZMaW2nDFtG/wsf1MCj3RpS2Uu3V8v/TMsNfYjDYOivEj+/luY38733DOLjyRbzvHWQUAoEJR83j32/fT1qTrXx1t9Kgr2LqKge/NrDdzUpxLlcXqV/5SMizBbzgey34MU4r6V+bzHQeHHmDfdmhW6xOHCnoVnY4PPsxH6AX+y/xYs7WbcbTU63hg6VxeaLkJR/Exw/tGEsnvh1cfVFMeXn1QfS1JkiSNa+No+WR8UBRlqaIo8xVFmZ9ijm2FciwllSaxUb9xWPccO3U+r1Rcx8O+OZiHma74P7oKrIbQ3cMOzawg1izVJL9gdnr43M5UcyoPWI6M8QmJVdGr5hdXW6oB8Keq6SJ76tRNbE1FA40cjp86H/s4iZvSfIJflZwXdLxdtCNOVd8QrVhmxZddFnqQvhSHTdqVG1yFR1BXrSNlYQq3ldzGnT++k94f9GLJ0G5VvWm1iT/+/RRedY2z4LezRV3xveZwePdJWeJMkiRpHItX0NsGJA0tF4a60upQFMUz6DqtJL30A+fGaqxJw3t4dJ/x64SOC4pPYcWsG/jYcipnOWJre5vfK7i27Bshz1cmhwmMolCZFr6l8B/KLiK/N3F1bUci2QcFyXlU6dUsnF6rWplhy5fqiu7GzIE3J4uSK4IHGEPXunMoSSkKOv7SrJfULxTBLmOYSgtfLlNXexu0y8xt6ZgPwMrD1Soj7aKdOwrv4K6f3IX/Mj+mVO3yZWs+tqjBr/Pm8RX87lwHf/gefKcQ/nGTbGcsSZI0DsUr6K0C9MD0IceH5t1WMSSXVghRCNgGXTcWY40LQidIOSkFa05sASjAxuLwq7w2o41rpp/P9hk38pxYwBGuyDm5kdzsKyPNkqZ5rtIwsspwlebQm+BOzDuSHzqiz/sdCxUpZbSKVszpZtyGTPwZBbQ0CIRe8JH5o/7rzvBH1yFttJgUuKPg7KDjKw0rST1Kfa/5zjsFKIYQ1TK+XBY6tcFkZdnbGdgL7TxjeybgXLNo5raS27j3Z/civiswJmmPv+YT8/gMfjua4Jnfw2XT4Ndnwco3ZLUHSZKkcSJeQe+nQCdqOTAAhBA21Lq4bwy67g3gNCHE4ATMbwFO4MMxHGtMWTIt6C/W88yvnmHxcYtpPSe2zTE6oy5k6+Ep9hx+O/NSaot/xn3emZRFmbcbjXQfLCk5R/PcAu/I8lQrFe3d+1aDlYeTTkCMz0Xefn0VHMx5ZhxKKj2p6ia25JnJ/SXYpqWWMtMz/jKNLupJ4fDsQ4KOrzxGXZ1tqhN0FYZY7a3fDe//U/NUR8EJdHcIOk/pxC+0czrqdfX8z/T/4cHFD2K4yIDBqv3mrC/4fcVxM47i6GtHJ5zfD5+/Brcsgsumw7N/VFMhJEmSpDETbUc2mxDiAiHEBUA+kN33WghhUxTFBfwO+KUQ4hohxEnAcwfG/+ugoR4E3MCLQoiThRBXArcCf+orPTZGY42J1INSab62md9e+1t+Xf5rNus3A/BA7gPYpg4/WEw+NJlWERgwZ1ozeXj21ezJuYJb3PlkeBMTJf7Mk8dU+5SgZ0/rHVkwd4jLgMUQnOd52/SLmTYOA8Wh5hjVFVwxRdDlSaGpV93E5p05kIZyRvbhYzK3SISAP2adGnT86aSnSalQ891X7p4beoAV2i2j11YfhM6g46mCpyLOoVZXyy0zb+HRJY9iuNiAJVM75/eL5Wb++PeTecVxM+2lp4cvqTba9u+Cv98AFxeoKRCfvAiOrrGelSRJ0ldOtJ9t56AGi4P1vS4FqlGDSx1wM5AJrAZOURSloe8GRVHaDgSe96HWym0H/owarA42qmONJp1Rh/1kO+/OfZc3LNqLzV2ii6ZvNGF/yK55PpSWOcErSVcVnc4VznSNq+PL7odbir7BtVuW9h9bkDnyPFUD6ma4zxq+GBg3Zy7XuaaEvmkcqVCSAHBluujostPjUHNV9+bt7b9mkakQxlm5tT4nOk0sKjie1/d+FHC86tQq8jbnsfxdGyd+uxh9s0bursbH+v7UHD55yUbS15LYrdsd9Tx26nZyS/kt2GfYuaz1MqZ/OJ2O9cH1f79YbuaL5UeSmlXJyac2UW76FNO+L6N+TkJ5XOpmt3efBIMRDjoOKs+EykVQNGkrKEqSJI0bQpH5ZiFNS5+mfK/9e3EZy5ptxXGmgyeKnojqH3ubYuPWh2/FUeeI+hkf//Jj3jO91/9aINhZfhOlcUxlCKdXwKzWf7GroxqA38y8hP91F4a/KQo/N27nnu3qeyyjzsia8sUc7Bq7zmXD0a5XSN95Jzftu4mFOxR2bTPTtE/HO798h+Wm5VgNVloLr8Myjv8abjL7mLvtLnyDKv8ZFAO/e/x3dFd3893L9jBtX+QW0QD7S77F0odns/GGjTxve35E8zrNdRqnrD8F57tOfB6NtoEHzJrn5dgFO8ltew9dR+OInpkwuWVq8Ft5Jsw9AUzaK9qSJElfZUKINYqizI/1/pHvYpLCSi5LZu/pe/lj9h/pET1R3+cQDhrObiD5wejqz1qyLCwzLgs4dnL+0aMW8AIYFbgt7yy+23EfAAv0WXEZt9I4sKp704xvTZiAF9TyX7n2qTQmN1K1vpCOFoHRbuRz4+cAnDB1wbgOeAHmuPVcWnomj+z6b/8xr/Cyb9E+Uh9I5Z33CimbY0D4Ii9Xf7puOkmlSbxgDd9hLxpvWd7ircq3KF5QzCU1l5D6dio9+4L/jlWtM1C1biZ6YznHn9LNvIIvSa79EOGNvWth3O3fBf+5T/1ltsGC0+EbP1EDYEmSJCkuxn9S5ASVOjeVmsU1LLlkCX/O+fOwAt4+90+5H3tBdCkOxgXGoE1BP0wd/VzRix0pzM1S0xoqPfFZrar0qikCczJmcosnuIzWeFeRWkaNpYaOFvUNiG2ODa9QA8RFSRPjY+3f6uZgMwbmmT+U/RC2KTYaanV0Fy0McecA79SZbFxlpPWkVhQRv0h/j9jD7cW3c/MVN1O9uJrUE1PRm4PfGPl6BcteT+bPS4/l/rU3s7vwMpSkcVj9w+1Q836XnAjXzIdlT0MUbygkSZKk8GTQG4ZiUUiZlYLeFP3KYupxqay7cR3XnXsdj6Q8EnJ3ejRcwkXd2XVRXbt7emDKRI4tm3OcSTE/O1ZCwB3Zp1KcUkhOnDbNTffoyLZl8Y+cb2Aa56uiWipseWw3DHTC7pk28AbozHFWqiyUPK/gutLA9sQO4aD962pTxNV7Do04xm79sehNep7MezIhc/QKL4+lPMZ1C6/jnpvuof5n9aSclILBEvyBVku9jiceKeHu937C1twr8KeNrLRewmxfA3deDN8rg+fvhp6J2SVSkiRpPJDpDWG02dpY/O3FGBQDh/gOYY5zDkXtRaQ1pGGoNeDc5cTT5UFn0GE7zcbL817mE6N2B6pYPZD9ALcX3k5PbfiV4rfS3wp4fWnByRjHqPXGmU4bO/NPV4vHxcljxZdwhHNi/rhWGDO5T9RgsBjwurzsmroLgJnp00c1/WSkbvQVs9SWRZOjuf/Yg/kPsiRtCR+/o7DwmwXoWvdq3qsIwQcf5mH7mo1aXW3/caPOyJLpF/LvhuX9ueDx0CyaeTD9QTgOMo7N4IKOC5i1cRaOTxx4XQOrpj2dgn8/UYDJ8iPOPLeJObymvSlvrDXVwtJfwFO3wRlXwLk/g5yJ96mHJEnSWJqYUcQo8wovXxi+4IvkLyAZKAQOpFGX+8vx42eHbkdCnu0WbmrPriXj/tAfw6bMTGG7bnvAsStEcULmE62fOrPjOt4iZ+wNO8ZahZKMIhSs+Va6dnbxWdJnACzKOgzGUVppJMk++E3RefykaqBCR6topffrvShPCvbYTqK09XHNe11FR1L3tp51F34ccPzKsrO5w1PM7WnFLMtz8w/nZl7cuwyX1xW3ebeKVpamLYVjIf2YdM7vOJ+KTRX0fNjTvwHO4xK89HQO/zVexulntzHP9haG+q1xm0PcODrhhT/By/fCcRfAhTfA9Mir7JIkSZJMbxixbbptCQt4+/wt628klYROVXDOC1xSXZh7BOVu+Z92vJjjVcuU6abqsOZY2aLfAsAiw8grW4y2H7mzmJEW2Fr64dKHMVgNvLusGEWnnQpU1Tmf5OnJvGx9uf+Y3Wjn16htpoWArznN/JNDqStezF9nXcm8rDlxn3+baOPvaX9n8TGL+cdN/8B/qR/blIFcZV+v4LUXMrjjqW/ziXkxnvwwdYjHks8LH/wbfnwY3Hou7A7fiVGSJEmSQe+E0Ct6qf56dcjzXxYF1iG9Mr0ywTOShiPTq+ZY92b3YpqjBsB2o53jXeOogUKUjArckxfYfa9WV4v+TD111Tp6io4PukcxWXn/rQwaTwwsF3Zd6blM0cj7TvfBta4s1iZ9gzWzbuTqaeeRatbuzDcS1bpqbiu9jd9c9Rvqf1pP6txBz1AE772awp2Pncszzb+kpui7+LLG9tOTkD59Ga6eC/93EezdNtazkSRJGrdk0DtBPJjxIMllweXLDBYDb9gGmlxkWDI43xldmTNp9FSkltGV3kVridox76SplRNyUx7AGQ4L3ywO7NT25Own0Rl0fLHvsKDrO/JPwOU28kTeE/3HsqyZLPFGXuk+zKXnAd8s9uddwzMVP+WC4lOCqkiMlEu4eDDjQa479zpW3LyC5DOS0RkG/q+xaq2JR/8xnduf/j7/7bmZ/SUX4k8dZ81R/H515feKCrjr+1BfPdYzkiRJGndk0DtOmPVmvpZ3FP836zIOzpwddN4rvOw4MziNwn64nS4x0NL0ksKTME/QYGoyq7Dn05zUTFVWFQCLkmaO8YxG5h7j4aSYU/pfb9Zvxna6jQ/fSsKfnhdw7bqag7CcZGG/2N9/7Jcl55Lij34Tn1WBCx1JPCcW0FT4c56r+BkXlpyG3Ti8roWRvGl+k+uPuJ4nf/kk4hKBJSuw7N7aT80sfbiC21+8ird9N9Jceg6KPfEdD6Pm98Hbj8Hl5XDv1dC8b6xnJEmSNG7IjmxhpE9LV9q/156QsQWCedlzODn9IE7W53Gcy4L1wH+KL80+Knf8GY8vsPyCQTHw+6d+T9fOgSDXfYWbOwvu7H+9ceaNzHFPnOYNXxX3W5t5e209K00rqdfVU1N+M4UTqHKDlvuszfxkUNvpI71Hcvodp3PZ93dSXKuWJfOn5vB//7maFdev5jXLawAUJRewLet7cXlz5hTwuqWH551bebXuE7o93SMfdBCbYuPsnrM5dNehiM8Ejv3BHRINJoVjTuqhPL+WLN9WjPWbxk/jC5MFzrxK3fCWmTvWs5EkSRqRkXZkk0FvGPEOeotTCjk561BOsZRwkttOli900PN/ljp+VfVY0PEftf2I3HsG/vHqa2cLcPTUw1luOS1u85XiZ5nVzVmb/4ZDODgocxYbks8b6ymNmB840vEaqxoHcsr/9NGfSNnTxuXT70D4/ewv+Rb//ugIFl+0uP+aR2dfw2XO+OfougS8Ye3hdXc1H/zEmDQAABx8SURBVLZsYHv7rriOLxTBiZ4TOX7f8aSsSaFzk3bNXItdYe4CFzNKm5hq2I6tcT3C0RHXuQybTgezj4JjzlV/5ZZFvkeSJGmckUFvAsUj6J2eVso3c47iQlHAPHf0FeJ8wHHON/ms4YuA4zpFx11P30Xntk5sU23ccNUN/ecSFUxII9dgUJi6Q12RX1L+bf7gmRxBx1qLlwVb78anqKW/TnOfxlF3HsUvfvg+9uqPeKH9V6w4zM1duXcBale99Snnj0peVZ1B4QNTFx+69/JB6wa2te2M6/iz/bNZ1LiIgg0FdK/q7i9/NpTQKcyc66ViViv5KbUkuWsxtu9B9CTmU6SolB0CRx8IgKeN0woVkiRJQ8igN4FiDXrLUkv45pSjuFBXyGGu2Esh7zApzNt9Lz29gY0prmi/goK/FGA/y86S+UsASDWnUpd/DbbYG8BJCZbV8BAtzhaWzf4FJzhNYz2duPm5cTv3bH+u//Wf3/wz87NrOTLtNf7wwvf4y5K/0CSaAHi54ud8wxHfjWjR2m9Q+NDYzQeevXzYupGqtu2Rb4pShpLBQtdCZrfMJqsmC98WX8SGMhk5foqme8jPdZKV2kaqoRl7bx3Gtj2Inra4zS2i3DI4+hw1AK44Wl0VliRJGodk0JtAmdMylI5LO/tXscIpSSnim1OP5kJdIfNdxrjN4QFrM9cMypuEA6u9z9zF1tO28lD6QwBcPe08HvDNittzpfg7zvU269u20ZJ77aTqCtOlh9kNj7GvW22ZfWHPhRz8l9mccU4ry9xFLDlMfWM23tJvtpv9PCP280zT52xsqYr7+IX+Qo51Hkt5Uzlp1Wl4NnlwNkXXpjA730/lkR1Mz9xGauOK0QuCjzobfv08GOL3/2GSJEnxIoPeBJo/s0xZ/cuL8Ah1w4xTr+AQCk7R97sfJ34yFQPzR7CiG8lpvR/x9r7A9saXdV7Ge0nv9bd0XTvrRua55Aa28ewq/Raavd08LxaM9VTi7kWbg/M3/6X/9Z9f+DMdGzpYfvNy3jG/A8BHs5dwnHN8BlObzX6eYR/PNH7G1rbENZsp95dT4a6gwFFAVmcWSS1J6Bv1eOu8OOocKP7g/z/W6RUOO8bNITNqmdq7FuP+zQmbH6Cu+N7yLOgn01szSZImAxn0JlBf0DvW6gwKB+19kDaX9mrP/JxDWGU7a5RnJQ3XvZYmkoSRy51pYz2VhPi671Nerf0AgB90/ICD3jmI6y64DoBFBQt5zXDMGM4ueussPp7x1/JMw3J2d+wZteeaFTOz/LOY7p5OUXcRBbsK6F3di6s5sCVzXqmPI45ooTSliqT9nyNc4dMoYnLcBfDLp2XgK0nSuCKD3gQaL0EvwNPWLi7e8lfNcw9VXM2VjnFUK1TS9J7NTYXHRK5GF7LJYI9RoaL6Hhy9DnSKjvOd5/Oc7Tl0QsfamUs4ZAJ+ErHK4uU9mljes5tPm9bT6mod1ecLRXCC9wSOrj+arC1ZdK/pxuceSLcyWxWO/lo3B+VvI71pOaI7jvNbeCHc9C/QT7z/bpIkTU4y6E2g8RT0AnybNTxT/VbAsSRTEnWFPyU5ctqxNMZ6hdrGdzL7o6WBG6r+EXDsOyWLeIp5YzSj+FEUqLL4Wa7rYLl7L8tbN8W9LFokyUoypztPZ27tXOwb7HRs6oADP1N6o0LlcU7mTt9Fdvun6NrqRv7AEy+CG56Uga8kSeOCDHoTaLwFva0GhYP2P8r+nvr+Y1dM+wYP++aM4awkaYAXOLzrZda3qHmnJr2JqmnXUzrBG3GE0mSAT00OlnsbWNdTw8b2nQF/PxOtxF/CWW1nUba1DNcKF57OAw1thMK8ozwcXrGHXOcK9I0jCM5P+i4seXziVHXo+zdNTM6fOUn6KpNBbwKNt6AX4A2bi0Wb/9T/+vNZN1CZwE10kjRcn1m9HLPljygoXDv9Av7qLR/rKY2qFgNsNHrYoHSysbeJDT01bGzbTqdbu5lFvJgVM2e6zuTwPYdjWW2ha8dA58bSWT7KZ3VRkNVIBnuwtlQhuluiH/yUS+H6R8Z/4NvRDL/5OmxZoc5VbwC9Uf3dYAz+2mQGkxXMVjBa1N9NVrWTXd/XthS1rvHMBZCZF3kOkiQljAx6E2g8Br0AP9JvZunOlzkks4Ivk88Z6+lIUpCr9Fv4Z+077Cz6CTmTNId5uGqMCpuMbupw0uB30uDroaG3k0ZPBw2uNhqczbQ6W1GIz/8nz/PN49TGU8ndmEv36sBcYICCMh8zK3oozGkmU1+Drb0KXXuYVerTLofFfx+/K6j7d8OvToe92xL3jMw8KJ8P5QvUILh8AaRkJO55kiQFkEFvAo3XoLdHB3NbnubnuadwrStrrKcjSUHa9Qr/NLVwjVP+fA6HF2gyKOwz+PhC18Xnrn183r6VLW3b8Suxd54xKkbm+uZS4aigsKOQtMY0DHsNOHc58XR4+q9bcLyL0wqeRd8UIh3ijB/Czx8af4HvjrVwyyJoHb3Ukn65ZWogPOtIOPxUKJHpZpKUKDLoTaDxGvSC+hHybI+eNN84+8dHkqS469LDapOHz5UWPnfU8HnLprjlDpf5y5jrnktpZymFmwpxr2nnkouryK15DuHXCLTPugp+8sD4CXxXvw23XwCOrsjXjoasAph/mvrr0JMhWVbWkaR4kUFvAo3noFeSpK+2vUaFFUYHn/bWs7xjK2ubN9Hr7x3xuN/v+D6z/z2b2dNbOaPoWe1NcJffCd++acTPGrF3n4Q//QC8I/++E0Knh1mVcPhpMP90NSVivOdFS9I4JoPeBJJBryRJE4VDB6vMHpYrzSzv3s2nzV/S7mqPaawsJYtfbP4FurfcfOdbm4NXfXV6uOsDOOjY+Ew+Fk/fCY/+MuCQYjBSk3cRtQ1Z+HwCn1+Hzyvw+QRen1C/9g58rdMrGPSKuq/N4MegV9Ab1GN6vR+9XsFk8pGV0kG6sQG7uxZD6+7YG4IkZ8DBx0NxBRTOhqJZUDgLrElx+AORpMlPBr0JJINeSZImKkWBzRYfy/WdrHbvp9rVRI2jgZqufTi9zqjGONlzMmf+90ympzdwRtFz6Bt3DpzMyocH1kJadoK+gxD8fnjgp/Df+wMOe6eU80r1hayfZmB31m56RS9e4cWr89IrevEIz8Dv9OLVeRF+gUmYMPqNGDBgVIwYFSMGvwGDYkCv6DH7zUztnkp6SzrmRjP+Oh9p+i6Kip3k5nSQZWsmRanD3LE7/EbAUIRQUyKKZqsBcNHsga/Tp8TpD02SJgcZ9CaQDHolSZqMmvQKNUYfNcJNDQ72eDuo8bSxqnULNV17A641KAaur7uenP8k8+2zN5Jb++zAqu/80+CON0Yvv9fjgjsvhuUv9R9ShKC++EJerFrAoyc8zRr9moRPw6gYmeObQ7m7nMKuQjLbMrE2WEntcVKa2UbR1GayTLUkdW5F11Ib+4OS0qBgpvqrsO/3WZA3XS23JklfMTLoTSAZ9EqS9FXiBZ61dfKHxvf5snlzwLnZ/tlc+cmVTPc0sij/SXStB4Ljy26Hi3+V+Ml1tsL/nA2blvcf8mcU8H7PJbxe5OIv2X9BEWP/71m2ks0R7iOY2TGTnIYcMjqg1NpGYUYzOZa9JDl3o2/fh3B1x/4QnR6mFKsBcOEsKJ6jpkwUzwFbcvy+GUkaZ2TQm0Ay6JUk6avqLauL33d8yrK6FQHHL+28lJM+nsHFMx5RS5vp9PCH9+CQhYmbTH01/OoMqK3qP9RadhbP7j+WP837B9t12/uPp1nSyLPlYBAGDDo9Rp0Bg9D3/zLqBr4GUFBQFLU6stL3P2Xga6/io87ZTG33fro8sVWIEIrgIP9BzHPMo7itmKTOJNI8kG10kalzkG7oJs3WRZKhDauvBZOjHl3HfoTHNfyHZRcGBsF9X8tgWJoEZNCbQDLolSTpq26VxcsfnGt5sea9/lrBB/sO5vr3L+Y7pY9iaNgGGbnwt3WQnhP/CWxbA78+E9oaAFCSs/jMchn/mNLKI6mP9F+WY8tmSdHZXO2Zgj32ksZhtesUakw+anW91CgOan3d1HjaqHE1UeOop7ZrX0z1lC2Khen+6ZT0lpDryiWrO4uUzmSyFT9TDC6ydT1kGLpIMbdjV5qwuOqHv1qcmg2ZuZCRd+B3jV+ZeTJtQhrXZNCbQDLolSRJUm03+7nLW8Xj1W/g9rnJVXK5/bOfcUnuUxjrNsJhp8D/vRnfklyfvwZ3fAsOVEvoLjmBpzwL+d/ih6jXqZvGpthzuKHwbK7y5GBLULAbLY+AapOfnXoPO5Vudnjb2OlqYmdPHbs69uD2uWMeO1lJZoZvBiWeEvKceWR0ZZDj05On7yVHOMhUukk1tpMkWrC4GzB07EX0xFC9I30K5JdD/gwoOPB7fjnkT1fbM0vSGJJBbwLJoFeSJClQg0HhN8oWlu58Gbti5+6Nt/D9lGcw7V0H37sNvvvr+DzotaXw1x+D34eSnMUXmRdxZ3odL9heACDXPpUbCr/Oj9zZWCfAP2OKAntNCtUGLw3CTaPiotHvpNHbQ6O3i0Z3O43uNhqdLbS52mJ6RqG/kBm9MyhyFzGlewrZHjMFBi9ThZt0nCT5ndh0PVh0XZh8nRg97eidrYjuZkSkWsdCqKkTfUFw3nQ1EM6fAVPL5AqxNCpk0JtAMuiVJEnS9p7NzQ/3PU91ew1/rPkN1+jewLJ3NfzuXZh3YuwDKwo8+iv4950oQtAy7ess1U/njswHcQgHeUm53Fh4Fle6srFM0n++egU0GhTq9T7qhYcGPNQrDuq93dT3dlLvbqPe1UK9o2nYecbpSjr5/nxyvDlke7NJ96ST7EomUzGQpfeTpfOShYd0xUmqrwc77Vi8LRidTeg6GxBuR/CgOp1adi3vQBCcN33gV8ZUtT6xbMohxYEMehNIBr2SJEmh9ejgJt027t/xAjc3/4xfuVZh69p5IL83hhqzvR64+3J4/594p87k1bQzuTntdar0VRQlF7CkYBE/dGVhlv9s9XMKaDAqNOi8NAovDbho8Dto9Dto6O2kwdNOg6uNlv9v7/5j5KjPO46/P7N7u7d3Z5/PZ4xt7NjgBMyPRkkrgYCWEmgTihIDaiBuq9I0StM0aqlC2xBSgtwmqYhaShJoglCFUhqFlqZUCQQaQdM0uCkqzg/aALETDCbml+3z/fTe7e7MPP3jO+dbrw8bn+/mrnvPSxrNzHdmT1/P4717dvaZ73dikJH6KBPx8T0c15/2s8E2cEr9FFbWV7I6rbC2ELFaCSendfqTKr31EbriAUrjr1IYfunIxDiKoHsZLO2fWpZk694VYbvSA53dR18KhVm8cu7/I09655Anvc45d2yPVRq87+Wv8IsvXchnhvfQszSCWx45vrt7B4dh61XYM4+z843X8LGuA9xffoCLVp/LdcvO58rxbjzlOXENwXBkjBSMEaVhiRKGrcEIDQatxkAyzv54jIF4jP31EQZqQ+wfH2S4NkwY5+JIa9I1bEw2sra+lvXpEjZEBdbKWGYxPWlMV1KnktbpjCcox+MUk4MU41Gi2ijRxAiMj6I0OXrnS53hrnHvivBg3tIVYbt13bcKVp8GnV1zcAXdfPKk93WQdBZwO3A+MAT8LfBnZnbUd5gnvc459/qMC24qPsvOp4e4ZzCl77wL4Nqtr+/Fe38KN13OWLSM25dt4pae+7h6/cVcVzqTN094qrtQJMBA0RgoGPuimL2qs89qoTY5GWNfY4y99WH21gbZNz7AgYnBaUeziCyin35WpCvoT/tZniynJ+6h18r0UaRXBXoV0WOwBOg2oytN6LKE7rROdzJOZ1ylnIxQjEcoTAwRVQfReFOphwT9p4SH8daeHuqQJ7dXnQqFYm7X7XUb3Auv7IJXngvLSevggit9uLkmnvQeg6Q+4CngaeDTwEbgVuA2M7vpaK/1pNc5547P45UGt73wI25/MWVl/ZVwd25y6eg8fD9rSx5/iEf638onlj3F5vVn8/54Ff3xfP9L3IlKgIORUY2gGhlVGVWlYU1CVQlVS8K2xVStQdUajFtMNa1PLUktWyYYaRxkqDbC6MQYy5JlrE5Wc1J8EsuT5SyPe1gZFTg5KrCSlH5ilqc1euMqPfUxOutDdEzspzAxjJYuD8lvuQKlytT/x3Jl6v9pOWsvd4XyikoPdPZMlWJUeqbamksvzMLsgc1LowaNbHv0ALz0bEhsX941leiOTzMEXbkC570TLv41OPfyRf/AoCe9xyDpRuAjwHozG8naPgJsBVZNtk3Hk17nnDt+NcEd6TBdu6GAEVlKQRClCQUZBcvaSInMeLKvxJvXdnsJgzsuNcFwwRgqGMNKGVbCsGKGqDOU1hlKawylEwwlVYbiKnHNqFR7qFS76amV6MfoQlQQFUVUTHQKyhaWTjNKGJ2W0p2kdMUNKnGNcqNGR1KlGB+kEB8kqo2ipA5pHOrSkzjcaVaUlfg0bSuCqIAVy1ihRFosY1GJNCqRRh2kKpGqSKoSHekoldHn0YE9yAy6e+HCq0IC/NZL57bGOUlg/x7Y+wK8uhv27g7be3eH/cFXYOUbYP05sOFs2HBOmAhl9WlzOi25J73HIOnbwEtmtqWp7Q3AbmCzmT3wWq/1pNc555xrTylhbOWajLqgLqMeiTpGPTJqpNQJx0eJGVPMGAmjaZ16AsWJTgoTnUQTJaLxEqoVQUBW+XxoWmwZdqg9HJtMC4UIDUJhWkAkgQlNFNBQiY7xlJXlYdaURljFAZY3XqUzOkjxnHPRxdfA6o0tSfVUcn1YW3UERgZgeD+M7J9at7TZ4KtQHSXtqJCWlpAUu2kUumlQoaEuapSp00GZOhU7SIkxSvEIxfoQUVpH/avQujNCErzuDCh0TCXCmvqXH7YfFaCjlH37U55ad3SGu9vZ+kST3gVY1DLrNgHfbG4wsxckVbNjr5n0UiiG2Wucc84511YioDNbZiTPUlt1M6xV7I8SdpLQGOtE1RLLtj9BOd2GSMEMQbgrbEYkQ1kbZqQmkkIHcRoRR0USImIrE7OOpHcDjaUR8SkFEiKijhQ6UqKOBIoJKiZQbFAoGGUiSkSMklAzwxod0OjAagWs3oHVI8qKqQxPUB7+CcIwC4k8CLOQ8prpULsBBaVESonI1hYjGbIkaz/Gg46vw2JIevsID6+1GsyOHUbSB4APZLtj2vz7O2axLyuA/bP489zMeSwWDo/FwuGxWDg8FguHx2LhOONEXrwYkt7jYmZ3AXfNxc+WtP1Ebsu72eOxWDg8FguHx2Lh8FgsHB6LhUPS9hN5/WKYImUQ6J2mvS875pxzzjnn2txiSHp/RKjdPUTSOqArO+acc84559rcYkh6HwbeIam55Pw9wDjwHzn3ZU7KJtyMeCwWDo/FwuGxWDg8FguHx2LhOKFYLIYhy/oIE1P8kDA5xWnAXwOfOdbkFM4555xzrj20fdILh6YhvoPDpyHeeqxpiJ1zzjnnXHtYFEmvc84555xb3BZDTW9uJF0t6WuSXpQ0Jum7ko6Y0k3S70j6saSJ7JxL56O/7UzSuyV9R9JAdp13SLpJUqnpHEn6mKSfShqX9G1Jb5nPfrc7Sadk7w2T1NPU7rHIgaT3Zte+dflg0zkei5xIKkr6aPb3oCZpj6TbWs7xeMwxSd96jfeFSTo/O8fjkBNJWyR9L/tb8aKkeyStaTlnRvHwpHd2XQ+MAR8GNgP/DnxZ0h9MnpAlwXcC9wC/AjwFPCjpnPy729b6CTPxvZ9wne8G/pRQzz3po8DHCbXe7yLE7lFJq/Lt6qLyl4Tr3Mpjka9LCOVek8v9Tcc8Fvn5InAd8FfA2wnXfrzlHI/H3PsQh78fzgceIUxI8UR2jschB5I2A/cC3wGuAG4ALgK+Lqk5Z51ZPMzMl1lagBXTtH0ZeK5pfwdwd9N+BPwv8KX57n+7L8CnCDXdIsw8OQzc3HS8G9gHfHK++9qOS/aL6wDwx4TZ5nuydo9FfjF4b/O1n+a4xyK/WFwGNICzjnKOx2N+YlPKfld9weOQ+7X/B+C7LW2bs99bZ55oPPxO7ywys+mmKfw+sAZA0mnA6cB9Ta9JgX8i3I10c2uA8MsM4AJgKYfH4iDwAB6LWSepANwO/DlHTufpsVg4PBb5eR/wTTN7+ijneDzmx2WECazuzfY9DvnpICS0zYaytbL1jOPhSe/cOx/YmW1PTpLROinGM8BySSfl1qtFQlJBUpeknyd8jfgFCx8LNwEJ8OOWlzxDy2QmblZ8ECgDfzPNMY9F/p6VFGe17r/b1O6xyM95wE5Jd0gakVSVdH9L7aLHY35sAfYAj2X7Hof83A38gqRrJS2VdDrwSQ7/gDjjeHjSO4eyB9SuBG7Nmvqy9VDLqYMtx93sOZgtjxEmI/mTrL0PGLMjh60bBLqaH3hzJ0ZSP/AJ4Hoza0xzisciPy8T6uB+k1AH9zhwp6QPZ8c9FvlZRSg3eQshyfpt4OeAf5E0eUfL45EzSV2Er9Pvy26QgMchN2b2dcL74i7CHd8dQAH41abTZhyP4qz21h0iaQOhnverZvbFee3M4nYBYcrpc4GbCeM1f2hee7T4fAp43Mwemu+OLHZm9g3gG01ND0vqBG6S9Nl56tZipWy5wswGACS9TPhwfgnwb/PYt8XsXYT60HuPdaKbfZLeRnjY/7OEGXVPBrYSPgz+0jSJ7nHxpHcOSFpOCNZu4DeaDk3e0e3l8Lu9fS3H3Swxs+9lm9sk7Qf+TtKthGvdI6nQ8ibqA6pmVs+7r+1I0tmE2sWLJC3Lmruyda+kBI/FfPsKcA2wAY9FngaBXZMJb2YbUAfOIiS9Ho/8bQF+Ymbbm9o8Dvm5Ffiamd0w2SDpB4Sy0CsII83MOB5e3jDLsq9GHiQ8MPVOM6s2HZ6s5W2tOdkEHDCzfTl0cTGbTIBPJcSiALyx5ZxNHFlz7WbuTYQHE/6L8ItqkKm63j2Eh9s8FvPLmtYei/w8w9SDOc0EpNm2xyNHknoJD0K13uX1OORnE/CD5gYz20EYym9j1jTjeHjSO4skFQkjMbwJuMzM9jYfN7NdhIfarm56TZTtP5xjVxerC7P1c4QxAEc4PBZdhK+2PBazZxvwtpbl09mxywnj9nos5te7CSNq7MZjkacHgZ+RtKKp7SLCh8Qns32PR76uIjxw25r0ehzysxv42eYGSWcCFeD5rGnG8fDyhtn1ecIf8j8E+rMHeCZ938xqhNqUL0l6HvhP4LcISfKv59vV9ibpX4FHCZN/JISE94+AfzSzZ7NzbgE+LmmQ8OnwesIHwdvnpdNtKBvG71vNbVm9O8BjZjaWtXksciDpn4H/Bv6HcKfkPdlyXTZ84oTHIjd3EUaUeUDSXwBLCB8IHzWzbQBm5vHI1xbgSTN7prnR45CrO4HbJL3EVE3vzYSE9yE4sXh40ju73p6tp3sg5FTgeTO7V2H61RsIT1E/RSiD+GFOfVwsniA8AboBiIFdwI2EN9SkWwhvkhsJM7htB37ZzF7Ns6MO8FjkZQehxnod4Wv0p4Frzezvm87xWOTAzEYkXQJ8jjAgfx34KmFGz2Yejxxkd9wvJfxdno7HIR+fI7wXfo8w1OUQ4RvDG7OxeCfNKB6aGpHDOeecc8659uQ1vc4555xzru150uucc84559qeJ73OOeecc67tedLrnHPOOefanie9zjnnnHOu7XnS65xzzjnn2p4nvc4555xzru150uucc84559re/wHmxguP6jDoTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting_age_structure(18, 81, 'LG Prediction', 0, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for NS prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting_age_structure(18, 81, 'NN Prediction', 1, 15)\n",
    "#plotting_age_structure(age_structure_unorm(data, 18, 81, 'neuro_acc.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting_age_structure(18, 81, 'NN Prediction', 0, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aposterior probabilities analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Town</th>\n",
       "      <th>Number of Subscriptions</th>\n",
       "      <th>$c_{1}^{NN}$</th>\n",
       "      <th>$c_{2}^{NN}$</th>\n",
       "      <th>$c_{3}^{NN}$</th>\n",
       "      <th>$c_{4}^{NN}$</th>\n",
       "      <th>$c_{1}^{LG}$</th>\n",
       "      <th>$c_{2}^{LG}$</th>\n",
       "      <th>$c_{3}^{LG}$</th>\n",
       "      <th>$c_{4}^{LG}$</th>\n",
       "      <th>LG Prediction</th>\n",
       "      <th>NN Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Верхняя Пышма</td>\n",
       "      <td></td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>Великий Новгород</td>\n",
       "      <td></td>\n",
       "      <td>13.000</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.263</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>Железнодорожный (Балашиха)</td>\n",
       "      <td></td>\n",
       "      <td>15.000</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.359</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>Петропавловск-Камчатский</td>\n",
       "      <td></td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.239</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td></td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.310</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex                        City Home Town  Number of Subscriptions  \\\n",
       "0   22    1               Верхняя Пышма                             12.000   \n",
       "1   22    2            Великий Новгород                             13.000   \n",
       "2   22    2  Железнодорожный (Балашиха)                             15.000   \n",
       "3   22    2    Петропавловск-Камчатский                             12.000   \n",
       "4   22    1             Санкт-Петербург                             20.000   \n",
       "\n",
       "   $c_{1}^{NN}$  $c_{2}^{NN}$  $c_{3}^{NN}$  $c_{4}^{NN}$  $c_{1}^{LG}$  \\\n",
       "0         0.319         0.320         0.309         0.051         0.275   \n",
       "1         0.142         0.181         0.560         0.118         0.264   \n",
       "2         0.208         0.134         0.101         0.556         0.256   \n",
       "3         0.181         0.381         0.132         0.306         0.261   \n",
       "4         0.223         0.402         0.206         0.169         0.268   \n",
       "\n",
       "   $c_{2}^{LG}$  $c_{3}^{LG}$  $c_{4}^{LG}$  LG Prediction  NN Prediction  \n",
       "0         0.244         0.226         0.255              1              2  \n",
       "1         0.233         0.240         0.263              1              3  \n",
       "2         0.219         0.166         0.359              4              4  \n",
       "3         0.283         0.217         0.239              2              2  \n",
       "4         0.236         0.186         0.310              4              2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note that the value $c_{i}^{LG}$ ($c_{i}^{NN}$) can be treated as a posterior probability of pertaining to the class i,\n",
    "#which, in turn, may be presented as a probability that this user support the correpsonding political player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct a scalar opinion space formalizing by the interval [0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For LG posterior probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['temp'] = data[['$c_{1}^{LG}$', '$c_{2}^{LG}$', '$c_{3}^{LG}$']].max(axis=1)\n",
    "\n",
    "data['Normalized $c_{4}^{LG}$'] = data[ '$c_{4}^{LG}$'] / data[['temp', '$c_{4}^{LG}$']].sum(axis=1)\n",
    "\n",
    "sns.distplot(data['Normalized $c_{4}^{LG}$'],\n",
    "             color='k', \n",
    "             kde=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For NN posterior probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['temp'] = data[['$c_{1}^{NN}$', '$c_{2}^{NN}$', '$c_{3}^{NN}$']].max(axis=1)\n",
    "\n",
    "data['Normalized $c_{4}^{NN}$'] = data[ '$c_{4}^{NN}$'] / data[['temp', '$c_{4}^{NN}$']].sum(axis=1)\n",
    "\n",
    "sns.distplot(data['Normalized $c_{4}^{NN}$'],\n",
    "             color='k', \n",
    "             kde=False)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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